2025-11-05 20:16:05.925052: 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`.
2025-11-05 20:16:05.936234: 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 00:00:1762370165.950201    1414 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762370165.954522    1414 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762370165.964910    1414 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762370165.964931    1414 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762370165.964933    1414 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762370165.964935    1414 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:16:05.968236: 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(
2025-11-05 20:16:08,960	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-05 20:16:09,690	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-05 20:16:09,764	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_56ec because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,767	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_c16a because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,770	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_6095 because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,772	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_1653 because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,774	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_5d7b because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,776	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_5b07 because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,778	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_d8dd because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,780	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_429c because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,783	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_37f5 because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,785	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_d7b3 because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,790	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_24bc because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,794	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_d8e2 because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,797	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_d425 because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,800	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_acec because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,804	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_a47a because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,807	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_73a3 because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,811	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_464b because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,815	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_5fcf because trial dirname 'dir_e2c7c' already exists.
2025-11-05 20:16:09,822	INFO trial.py:182 -- Creating a new dirname dir_e2c7c_ce0b because trial dirname 'dir_e2c7c' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     ESANN_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator          │
│ Scheduler                        AsyncHyperBandScheduler        │
│ Number of trials                 20                             │
╰─────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_ESANN_acc_17_classes/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-05_20-16-08_253510_1414/artifacts/2025-11-05_20-16-09/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-05 20:16:09. 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     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    PENDING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    PENDING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    PENDING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    PENDING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    PENDING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    PENDING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    PENDING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    PENDING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    PENDING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    PENDING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    PENDING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    PENDING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    PENDING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    PENDING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    PENDING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    PENDING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    PENDING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    PENDING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    PENDING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    PENDING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            77 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00058 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_e2c7c config            │
├─────────────────────────────────────┤
│ N_capas                           4 │
│ epochs                          115 │
│ funcion_activacion             tanh │
│ numero_filtros                  128 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                 32 │
│ tasa_aprendizaje             0.0041 │
╰─────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           120 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje              0.0028 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            57 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00021 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            59 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00487 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           125 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00087 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            75 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00133 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            53 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00354 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           141 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00492 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            58 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           145 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00061 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
[36m(train_cnn_ray_tune pid=3178)[0m 2025-11-05 20:16:12.931827: 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=3178)[0m 2025-11-05 20:16:12.952049: 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=3178)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=3178)[0m E0000 00:00:1762370172.978206    4422 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=3178)[0m E0000 00:00:1762370172.985989    4422 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=3198)[0m W0000 00:00:1762370173.087400    4437 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=3198)[0m W0000 00:00:1762370173.087455    4437 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=3198)[0m W0000 00:00:1762370173.087458    4437 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=3198)[0m W0000 00:00:1762370173.087460    4437 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=3198)[0m 2025-11-05 20:16:13.093974: 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=3198)[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=3178)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
[36m(train_cnn_ray_tune pid=3178)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=3198)[0m 2025-11-05 20:16:16.339270: 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=3198)[0m 2025-11-05 20:16:16.339319: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=3198)[0m 2025-11-05 20:16:16.339327: 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=3198)[0m 2025-11-05 20:16:16.339332: 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=3198)[0m 2025-11-05 20:16:16.339337: 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=3198)[0m 2025-11-05 20:16:16.339341: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=3198)[0m 2025-11-05 20:16:16.339571: 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=3198)[0m 2025-11-05 20:16:16.339603: 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=3198)[0m 2025-11-05 20:16:16.339607: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           143 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00033 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_e2c7c config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                           52 │
│ funcion_activacion             relu │
│ numero_filtros                  128 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                 64 │
│ tasa_aprendizaje             0.0009 │
╰─────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            60 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           148 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00053 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           128 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00006 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_e2c7c config            │
├─────────────────────────────────────┤
│ N_capas                           3 │
│ epochs                          136 │
│ funcion_activacion             relu │
│ numero_filtros                   32 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 64 │
│ tasa_aprendizaje             0.0001 │
╰─────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            53 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00017 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            57 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_e2c7c started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_e2c7c config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            96 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje              0.0042 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3178)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=3178)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=3178)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=3178)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=3178)[0m │ conv1d (Conv1D)                 │ (None, 3, 128)         │        96,128 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ layer_normalization             │ (None, 3, 128)         │           256 │
[36m(train_cnn_ray_tune pid=3178)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ dropout (Dropout)               │ (None, 3, 128)         │             0 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        49,280 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ layer_normalization_1           │ (None, 3, 128)         │           256 │
[36m(train_cnn_ray_tune pid=3178)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ conv1d_2 (Conv1D)               │ (None, 3, 128)         │        49,280 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ layer_normalization_2           │ (None, 3, 128)         │           256 │
[36m(train_cnn_ray_tune pid=3178)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ dropout_2 (Dropout)             │ (None, 3, 128)         │             0 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ conv1d_3 (Conv1D)               │ (None, 3, 128)         │        49,280 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ layer_normalization_3           │ (None, 3, 128)         │           256 │
[36m(train_cnn_ray_tune pid=3178)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ dropout_3 (Dropout)             │ (None, 3, 128)         │             0 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ global_average_pooling1d        │ (None, 128)            │             0 │
[36m(train_cnn_ray_tune pid=3178)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ dropout_4 (Dropout)             │ (None, 128)            │             0 │
[36m(train_cnn_ray_tune pid=3178)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3178)[0m │ dense (Dense)                   │ (None, 15)             │         1,935 │
[36m(train_cnn_ray_tune pid=3178)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=3178)[0m  Total params: 246,927 (964.56 KB)
[36m(train_cnn_ray_tune pid=3178)[0m  Trainable params: 246,927 (964.56 KB)
[36m(train_cnn_ray_tune pid=3178)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=3198)[0m  Total params: 409,231 (1.56 MB)
[36m(train_cnn_ray_tune pid=3198)[0m  Trainable params: 409,231 (1.56 MB)
[36m(train_cnn_ray_tune pid=3178)[0m Epoch 1/77
[36m(train_cnn_ray_tune pid=3202)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20:32[0m 2s/step - accuracy: 0.1250 - loss: 5.2307
[1m  4/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0801 - loss: 4.9024
[36m(train_cnn_ray_tune pid=3202)[0m 
[1m  9/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 15ms/step - accuracy: 0.0780 - loss: 4.7561 
[1m 13/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 14ms/step - accuracy: 0.0790 - loss: 4.6675
[36m(train_cnn_ray_tune pid=3202)[0m 
[1m 17/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 14ms/step - accuracy: 0.0787 - loss: 4.5946
[1m 21/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 14ms/step - accuracy: 0.0770 - loss: 4.5432
[36m(train_cnn_ray_tune pid=3202)[0m 
[1m 25/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 15ms/step - accuracy: 0.0758 - loss: 4.4988
[36m(train_cnn_ray_tune pid=3190)[0m 
[1m  7/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.0601 - loss: 4.0638
[1m  9/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.0634 - loss: 4.0502
[36m(train_cnn_ray_tune pid=3201)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:00[0m 3s/step - accuracy: 0.0547 - loss: 4.2915
[36m(train_cnn_ray_tune pid=3190)[0m 
[1m 17/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.0678 - loss: 4.0192
[36m(train_cnn_ray_tune pid=3185)[0m Model: "sequential"[32m [repeated 19x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)[0m
[36m(train_cnn_ray_tune pid=3185)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m │ global_average_pooling1d        │ (None, 128)            │             0 │[32m [repeated 114x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 209x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m │ layer_normalization             │ (None, 3, 128)         │           256 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 57x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m │ dropout (Dropout)               │ (None, 3, 128)         │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m │ dropout_4 (Dropout)             │ (None, 128)            │             0 │[32m [repeated 57x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m │ dense (Dense)                   │ (None, 15)             │         1,935 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m  Total params: 246,927 (964.56 KB)[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m  Trainable params: 246,927 (964.56 KB)[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3203)[0m  Total params: 409,231 (1.56 MB)[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3203)[0m  Trainable params: 409,231 (1.56 MB)[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m Epoch 1/57[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3202)[0m 
[1m127/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 22ms/step - accuracy: 0.0776 - loss: 3.9286 
[1m130/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 22ms/step - accuracy: 0.0778 - loss: 3.9178
[36m(train_cnn_ray_tune pid=3200)[0m 
[1m  3/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 83ms/step - accuracy: 0.1042 - loss: 4.7076 
[1m  4/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 80ms/step - accuracy: 0.1064 - loss: 4.6781
[36m(train_cnn_ray_tune pid=3178)[0m 
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 98ms/step - accuracy: 0.0747 - loss: 4.5999 
[36m(train_cnn_ray_tune pid=3207)[0m 
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 54ms/step - accuracy: 0.0813 - loss: 4.1918
[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 54ms/step - accuracy: 0.0813 - loss: 4.1869
[1m 43/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 54ms/step - accuracy: 0.0813 - loss: 4.1819
[36m(train_cnn_ray_tune pid=3186)[0m 
[1m132/146[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 26ms/step - accuracy: 0.0708 - loss: 3.9168
[1m134/146[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 26ms/step - accuracy: 0.0708 - loss: 3.9143
[1m136/146[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 26ms/step - accuracy: 0.0709 - loss: 3.9118
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m  2/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 61ms/step - accuracy: 0.1016 - loss: 4.5129  
[36m(train_cnn_ray_tune pid=3184)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:01:34[0m 6s/step - accuracy: 0.0938 - loss: 3.8936
[1m  2/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 71ms/step - accuracy: 0.1016 - loss: 3.9178  [32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3205)[0m 
[1m 54/292[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 42ms/step - accuracy: 0.0710 - loss: 4.3485 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m Epoch 2/145
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 2/57
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 2/58[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 3/57[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 20:16:39. Total running time: 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    RUNNING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    RUNNING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    RUNNING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    RUNNING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    RUNNING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    RUNNING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 4/57[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m Epoch 2/57[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m Epoch 6/145[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 4/136[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 3/75[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 20:17:10. Total running time: 1min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    RUNNING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    RUNNING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    RUNNING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    RUNNING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    RUNNING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    RUNNING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 5/148[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m Epoch 5/77[32m [repeated 7x across cluster][0m
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 8/57[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 4/75[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m Epoch 6/77[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3178)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 20:17:40. Total running time: 1min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    RUNNING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    RUNNING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    RUNNING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    RUNNING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    RUNNING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    RUNNING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m Epoch 5/53[32m [repeated 5x across cluster][0m
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 12/53[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 8/148[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m Epoch 11/96[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m Epoch 5/57[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 15/53[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3205)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 20:18:10. Total running time: 2min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    RUNNING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    RUNNING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    RUNNING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    RUNNING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    RUNNING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    RUNNING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m Epoch 4/141[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 6/75[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m Epoch 8/52[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m Epoch 4/143[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m Epoch 23/145[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 16/57[32m [repeated 6x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-11-05 20:18:40. Total running time: 2min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    RUNNING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    RUNNING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    RUNNING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    RUNNING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    RUNNING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    RUNNING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m Epoch 16/96[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m Epoch 25/145[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m Epoch 26/145[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m Epoch 27/145[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m Epoch 12/77[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 20:19:10. Total running time: 3min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    RUNNING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    RUNNING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    RUNNING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    RUNNING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    RUNNING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    RUNNING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3187)[0m Epoch 14/136[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m Epoch 8/57[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 15/136[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 21/57[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m Epoch 12/52[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 27/53[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 20:19:40. Total running time: 3min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    RUNNING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    RUNNING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    RUNNING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    RUNNING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    RUNNING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    RUNNING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3185)[0m Epoch 9/57[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 17/136[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 16/58[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m Epoch 39/145[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 20:20:10. Total running time: 4min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING            4   adam            relu                                  128                128                  3          0.000576404         77 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   64                128                  3          8.5657e-05          57 │
│ trial_e2c7c    RUNNING            4   adam            relu                                   32                 64                  5          0.00487194          59 │
│ trial_e2c7c    RUNNING            2   adam            relu                                  128                 32                  3          0.000611667        145 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                 32                  3          0.000100792        136 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                   32                128                  5          0.00409611         115 │
│ trial_e2c7c    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.00133012          75 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   32                128                  5          0.000868639        125 │
│ trial_e2c7c    RUNNING            3   adam            tanh                                   64                128                  3          5.91221e-05        128 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   32                128                  3          0.00280181         120 │
│ trial_e2c7c    RUNNING            4   rmsprop         relu                                   32                128                  5          0.000327411        143 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                  128                 64                  5          0.000211066         57 │
│ trial_e2c7c    RUNNING            2   adam            relu                                   64                128                  5          0.000897052         52 │
│ trial_e2c7c    RUNNING            3   rmsprop         relu                                   32                128                  5          1.46437e-05         60 │
│ trial_e2c7c    RUNNING            4   adam            tanh                                  128                 64                  3          0.000533151        148 │
│ trial_e2c7c    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00419526          96 │
│ trial_e2c7c    RUNNING            3   adam            relu                                  128                 32                  5          0.000166185         53 │
│ trial_e2c7c    RUNNING            3   adam            relu                                   64                128                  3          0.00353537          53 │
│ trial_e2c7c    RUNNING            4   rmsprop         tanh                                   32                128                  3          0.00492245         141 │
│ trial_e2c7c    RUNNING            2   rmsprop         relu                                   64                128                  3          1.33251e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3199)[0m Epoch 32/53[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
[1m320/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m13s[0m 51ms/step - accuracy: 0.1340 - loss: 2.5504[32m [repeated 163x across cluster][0m
[36m(train_cnn_ray_tune pid=3191)[0m 
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m27s[0m 47ms/step - accuracy: 0.2366 - loss: 2.0952 - val_accuracy: 0.2664 - val_loss: 1.9534[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m Epoch 17/77[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m Epoch 42/145[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m Epoch 27/96[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
[1m146/146[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 58ms/step - accuracy: 0.2028 - loss: 2.1943 - val_accuracy: 0.2653 - val_loss: 1.9678[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3201)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 127ms/step - accuracy: 0.1953 - loss: 2.3200[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 142ms/step - accuracy: 0.1484 - loss: 2.1967
[1m  2/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 77ms/step - accuracy: 0.1758 - loss: 2.1437 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 49/146[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 52ms/step - accuracy: 0.1864 - loss: 2.3475
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 35/53[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 19/58[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[1m135/146[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 58ms/step - accuracy: 0.1475 - loss: 2.5241[32m [repeated 210x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
[1m36/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[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=3186)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3189)[0m 2025-11-05 20:16:13.684799: 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=3189)[0m 2025-11-05 20:16:13.703168: 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=3189)[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=3189)[0m E0000 00:00:1762370173.726798    4564 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=3189)[0m E0000 00:00:1762370173.733542    4564 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=3189)[0m W0000 00:00:1762370173.751403    4564 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=3189)[0m 2025-11-05 20:16:13.757110: 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=3189)[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=3185)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 2025-11-05 20:16:17.085844: 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=3185)[0m 2025-11-05 20:16:17.085961: 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=3185)[0m 2025-11-05 20:16:17.085984: 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=3185)[0m 2025-11-05 20:16:17.085997: 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=3185)[0m 2025-11-05 20:16:17.086011: 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=3185)[0m 2025-11-05 20:16:17.086022: 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=3185)[0m 2025-11-05 20:16:17.086786: 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=3185)[0m 2025-11-05 20:16:17.086888: 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=3185)[0m 2025-11-05 20:16:17.086896: 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=3199)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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[36m(train_cnn_ray_tune pid=3186)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:20:39. Total running time: 4min 30s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              267.03 │
│ time_total_s                  267.03 │
│ training_iteration                 1 │
│ val_accuracy                 0.27567 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:20:39. Total running time: 4min 30s
[36m(train_cnn_ray_tune pid=3191)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-05 20:20:40. Total running time: 4min 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     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            relu                                  128                128                  3          0.000576404         77                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         relu                                  128                 64                  5          0.000211066         57                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         relu                                   32                128                  5          1.46437e-05         60                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         tanh                                  128                128                  3          0.00419526          96                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                  128                 32                  5          0.000166185         53                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         tanh                                   32                128                  3          0.00492245         141                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1             267.03         0.275671 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m Epoch 9/59[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 38/53[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m10s[0m 51ms/step - accuracy: 0.2752 - loss: 1.9641[32m [repeated 138x across cluster][0m
[36m(train_cnn_ray_tune pid=3190)[0m 
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 37ms/step - accuracy: 0.1453 - loss: 2.5172 - val_accuracy: 0.1462 - val_loss: 2.4743[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 156ms/step - accuracy: 0.2109 - loss: 2.3472[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 5/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step  
[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 8/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m16/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m19/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[36m(train_cnn_ray_tune pid=3201)[0m 
[1m  2/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 71ms/step - accuracy: 0.1934 - loss: 2.1672 
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m21/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m31/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m49/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m73/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m78/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m85/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
[36m(train_cnn_ray_tune pid=3188)[0m 
[1m103/146[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 59ms/step - accuracy: 0.1449 - loss: 2.5261
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m  8/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 14/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3178)[0m Epoch 20/77[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 18/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 22/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 25/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 31/169[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 34/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 37/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[1m 40/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[1m 42/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3187)[0m 
[1m  3/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 31ms/step - accuracy: 0.1189 - loss: 2.6091  
[1m  5/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 30ms/step - accuracy: 0.1276 - loss: 2.6078
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 46/169[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[1m 49/169[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 51/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[1m 54/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[36m(train_cnn_ray_tune pid=3207)[0m 
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m7s[0m 52ms/step - accuracy: 0.2744 - loss: 1.9556
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m7s[0m 52ms/step - accuracy: 0.2744 - loss: 1.9555[32m [repeated 298x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m514/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 55ms/step - accuracy: 0.1388 - loss: 2.5320[32m [repeated 344x across cluster][0m
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 57/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[1m 61/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 65/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[1m 70/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3199)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 113ms/step - accuracy: 0.1094 - loss: 2.4291
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 31ms/step - accuracy: 0.1276 - loss: 2.4135  
[36m(train_cnn_ray_tune pid=3178)[0m 
[1m 18/146[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 78ms/step - accuracy: 0.2216 - loss: 2.1116 
[1m 19/146[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 77ms/step - accuracy: 0.2213 - loss: 2.1116[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3178)[0m 
[1m  2/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 62ms/step - accuracy: 0.2480 - loss: 2.1227  
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 73/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[1m 76/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 79/169[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[1m 82/169[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 86/169[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 18ms/step
[1m 90/169[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3184)[0m 
[1m255/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m15s[0m 47ms/step - accuracy: 0.1830 - loss: 2.2954
[1m257/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m15s[0m 47ms/step - accuracy: 0.1830 - loss: 2.2954[32m [repeated 33x across cluster][0m
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 93/169[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 18ms/step
[1m 96/169[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m 99/169[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 18ms/step
[1m104/169[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m108/169[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 18ms/step
[1m112/169[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m115/169[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 18ms/step
[1m119/169[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m122/169[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3184)[0m 
[1m271/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m14s[0m 47ms/step - accuracy: 0.1831 - loss: 2.2951[32m [repeated 66x across cluster][0m
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m125/169[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m129/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3199)[0m 
[1m146/146[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 43ms/step - accuracy: 0.1638 - loss: 2.4046 - val_accuracy: 0.2118 - val_loss: 2.1578[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3204)[0m 
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
[1m140/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=3204)[0m 
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[36m(train_cnn_ray_tune pid=3204)[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=3204)[0m   _log_deprecation_warning(
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[36m(train_cnn_ray_tune pid=3188)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:20:58. Total running time: 4min 49s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             285.961 │
│ time_total_s                 285.961 │
│ training_iteration                 1 │
│ val_accuracy                 0.13284 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:20:58. Total running time: 4min 49s
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 21/58[32m [repeated 8x across cluster][0m
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 33/57[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-05 20:21:10. Total running time: 5min 0s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            relu                                  128                128                  3          0.000576404         77                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         relu                                  128                 64                  5          0.000211066         57                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         tanh                                  128                128                  3          0.00419526          96                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                  128                 32                  5          0.000166185         53                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         tanh                                   32                128                  3          0.00492245         141                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 41/53[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 28/148[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m Epoch 17/53[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 29/148[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 137ms/step - accuracy: 0.2188 - loss: 2.0379[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 36/57[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
[1m 6/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step  
[1m 9/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
[1m40/86[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=3206)[0m 
[1m44/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
[1m542/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 52ms/step - accuracy: 0.1490 - loss: 2.5168[32m [repeated 360x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m6s[0m 46ms/step - accuracy: 0.2870 - loss: 1.9097
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[36m(train_cnn_ray_tune pid=3206)[0m 
[1m47/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=3206)[0m 
[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 17ms/step
[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 17ms/step
[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m Epoch 18/53[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3206)[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=3206)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3206)[0m 
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[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 19ms/step

Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:21:37. Total running time: 5min 27s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             324.274 │
│ time_total_s                 324.274 │
│ training_iteration                 1 │
│ val_accuracy                  0.1408 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:21:37. Total running time: 5min 27s
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-05 20:21:40. Total running time: 5min 30s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            relu                                  128                128                  3          0.000576404         77                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         relu                                  128                 64                  5          0.000211066         57                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         tanh                                  128                128                  3          0.00419526          96                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                  128                 32                  5          0.000166185         53                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m Epoch 15/120[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 47/53[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 32/148[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m Epoch 38/96[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m Epoch 22/52[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 41/57[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-05 20:22:10. Total running time: 6min 0s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            relu                                  128                128                  3          0.000576404         77                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         relu                                  128                 64                  5          0.000211066         57                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         tanh                                  128                128                  3          0.00419526          96                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                  128                 32                  5          0.000166185         53                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m Epoch 10/115[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 52/53[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 28/58[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m Epoch 53/53[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m Epoch 28/77[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[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=3199)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m Epoch 45/57[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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[36m(train_cnn_ray_tune pid=3199)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:22:37. Total running time: 6min 27s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             384.439 │
│ time_total_s                 384.439 │
│ training_iteration                 1 │
│ val_accuracy                 0.22553 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:22:37. Total running time: 6min 27s
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 35ms/step - accuracy: 0.2040 - loss: 2.1368  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3191)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-05 20:22:40. Total running time: 6min 30s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            relu                                  128                128                  3          0.000576404         77                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         relu                                  128                 64                  5          0.000211066         57                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         tanh                                  128                128                  3          0.00419526          96                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3178)[0m 
[1m  2/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 65ms/step - accuracy: 0.2891 - loss: 2.0320  
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 66ms/step - accuracy: 0.2865 - loss: 2.0175
[36m(train_cnn_ray_tune pid=3178)[0m Epoch 29/77[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3178)[0m 
[1m 15/146[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 57ms/step - accuracy: 0.2582 - loss: 2.0404
[1m 16/146[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 57ms/step - accuracy: 0.2578 - loss: 2.0412[32m [repeated 197x across cluster][0m
[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
[1m 58/292[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 43ms/step - accuracy: 0.3058 - loss: 1.8472 
[1m 60/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 42ms/step - accuracy: 0.3056 - loss: 1.8477
[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 18/75[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m Epoch 23/53[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 31/58[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m Epoch 14/59[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m Epoch 47/96[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-05 20:23:10. Total running time: 7min 0s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            relu                                  128                128                  3          0.000576404         77                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         relu                                  128                 64                  5          0.000211066         57                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         tanh                                  128                128                  3          0.00419526          96                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3201)[0m Epoch 50/57[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 33/58[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 282ms/step
[1m 6/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step  
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m11/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3189)[0m 
[1m22/86[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3189)[0m 
[1m32/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3189)[0m 
[1m47/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m51/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3207)[0m Epoch 25/53[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 12ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m72/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m76/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step
[1m81/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m84/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3197)[0m 
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 32ms/step - accuracy: 0.1168 - loss: 3.1118
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 32ms/step - accuracy: 0.1168 - loss: 3.1116[32m [repeated 222x across cluster][0m
[36m(train_cnn_ray_tune pid=3202)[0m 
[1m505/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3096 - loss: 1.8710[32m [repeated 212x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 17ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 58ms/step
[1m  5/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 11/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[1m 16/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 19/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[1m 25/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 31/169[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[1m 36/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 40/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 45/169[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[1m 49/169[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 53/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[1m 58/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3191)[0m 
[1m134/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 33ms/step - accuracy: 0.2991 - loss: 1.8860
[1m136/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 33ms/step - accuracy: 0.2992 - loss: 1.8858[32m [repeated 101x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 70/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[1m 74/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m236/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m15s[0m 45ms/step - accuracy: 0.1417 - loss: 2.5169[32m [repeated 110x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 78/169[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[1m 82/169[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3188)[0m 
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 45ms/step - accuracy: 0.1602 - loss: 2.4198  
[1m  4/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 48ms/step - accuracy: 0.1616 - loss: 2.4182
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 86/169[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m 90/169[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m 96/169[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3188)[0m 
[1m146/146[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 55ms/step - accuracy: 0.1695 - loss: 2.4414 - val_accuracy: 0.2107 - val_loss: 2.3102[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3189)[0m 
[1m100/169[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=3189)[0m 
[1m111/169[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3189)[0m 
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[36m(train_cnn_ray_tune pid=3189)[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=3189)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3189)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:23:25. Total running time: 7min 15s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             432.145 │
│ time_total_s                 432.145 │
│ training_iteration                 1 │
│ val_accuracy                 0.24144 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:23:25. Total running time: 7min 15s
[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m Epoch 21/120[32m [repeated 5x across cluster][0m
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 44/148[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 21/75[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-11-05 20:23:40. Total running time: 7min 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     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            relu                                  128                128                  3          0.000576404         77                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         relu                                  128                 64                  5          0.000211066         57                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m Epoch 13/115[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[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=3201)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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[36m(train_cnn_ray_tune pid=3201)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:23:45. Total running time: 7min 35s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             452.634 │
│ time_total_s                 452.634 │
│ training_iteration                 1 │
│ val_accuracy                 0.25273 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:23:45. Total running time: 7min 35s
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 36/58[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m Epoch 31/52[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 37/58[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 41/136[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 49/148[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3178)[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=3178)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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[36m(train_cnn_ray_tune pid=3178)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:24:09. Total running time: 7min 59s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              476.71 │
│ time_total_s                  476.71 │
│ training_iteration                 1 │
│ val_accuracy                 0.24884 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:24:09. Total running time: 7min 59s
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 45ms/step - accuracy: 0.1429 - loss: 2.5144 - val_accuracy: 0.1525 - val_loss: 2.4380[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[1m 52/292[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m6s[0m 25ms/step - accuracy: 0.1178 - loss: 3.0178

Trial status: 7 TERMINATED | 13 RUNNING
Current time: 2025-11-05 20:24:10. Total running time: 8min 0s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[1m  4/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 17ms/step - accuracy: 0.1878 - loss: 2.3567 
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m Epoch 24/120[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m Epoch 20/125[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m Epoch 15/143[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 52/148[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 25/75[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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Trial status: 7 TERMINATED | 13 RUNNING
Current time: 2025-11-05 20:24:40. Total running time: 8min 30s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                128                  3          0.00353537          53                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m Epoch 36/52[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m Epoch 43/58[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m Epoch 27/120[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m Epoch 57/148[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m Epoch 38/52[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[0m 
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[36m(train_cnn_ray_tune pid=3207)[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=3207)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3207)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:25:08. Total running time: 8min 58s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             535.643 │
│ time_total_s                 535.643 │
│ training_iteration                 1 │
│ val_accuracy                 0.30842 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:25:08. Total running time: 8min 58s
[36m(train_cnn_ray_tune pid=3203)[0m Epoch 17/143[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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Trial status: 8 TERMINATED | 12 RUNNING
Current time: 2025-11-05 20:25:10. Total running time: 9min 0s
Logical resource usage: 12.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   32                128                  3          0.00280181         120                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                128                  3          0.00353537          53        1            535.643         0.308418 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m Epoch 27/57[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m Epoch 29/120[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 28/75[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 82ms/step - accuracy: 0.1875 - loss: 2.1670
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m Epoch 18/143[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 21ms/step - accuracy: 0.3397 - loss: 1.8019 - val_accuracy: 0.3321 - val_loss: 1.7744[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[0m 
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[36m(train_cnn_ray_tune pid=3202)[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=3202)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3188)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:25:33. Total running time: 9min 23s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             560.751 │
│ time_total_s                 560.751 │
│ training_iteration                 1 │
│ val_accuracy                  0.3321 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:25:33. Total running time: 9min 23s
[36m(train_cnn_ray_tune pid=3188)[0m Epoch 63/148[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
[1m133/146[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 29ms/step - accuracy: 0.2111 - loss: 2.2171
[1m135/146[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 29ms/step - accuracy: 0.2110 - loss: 2.2171
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
[1m38/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m43/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m48/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3198)[0m 
[1m58/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 11ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[0m 
[1m73/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 11ms/step
[1m78/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m83/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3188)[0m Epoch 64/148[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 51ms/step
[36m(train_cnn_ray_tune pid=3197)[0m 
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.1333 - loss: 2.7871
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.1333 - loss: 2.7872[32m [repeated 241x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.1333 - loss: 2.7872[32m [repeated 162x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m  6/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[1m 11/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 17/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[1m 22/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 25/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 34/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 39/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[1m 43/169[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 48/169[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[1m 53/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 59/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3191)[0m 
[1m 54/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 19ms/step - accuracy: 0.3315 - loss: 1.7711
[1m 57/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 19ms/step - accuracy: 0.3319 - loss: 1.7713 [32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 69/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 78/169[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 83/169[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 88/169[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m 93/169[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3198)[0m 
[1m102/169[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 11ms/step
[1m107/169[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3191)[0m 
[1m 13/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2983 - loss: 1.7886[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m111/169[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
[1m116/169[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3188)[0m 
[1m146/146[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 34ms/step - accuracy: 0.2107 - loss: 2.2168 - val_accuracy: 0.2178 - val_loss: 2.0473[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m121/169[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 11ms/step
[1m127/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 11ms/step

Trial status: 9 TERMINATED | 11 RUNNING
Current time: 2025-11-05 20:25:40. Total running time: 9min 30s
Logical resource usage: 11.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                   32                128                  5          0.00409611         115                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   64                128                  5          0.000897052         52                                              │
│ trial_e2c7c    RUNNING              4   adam            tanh                                  128                 64                  3          0.000533151        148                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   32                128                  3          0.00280181         120        1            560.751         0.3321   │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                128                  3          0.00353537          53        1            535.643         0.308418 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3198)[0m 
[1m132/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3198)[0m 
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[36m(train_cnn_ray_tune pid=3198)[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=3198)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3198)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:25:41. Total running time: 9min 31s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             568.662 │
│ time_total_s                 568.662 │
│ training_iteration                 1 │
│ val_accuracy                 0.16355 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:25:41. Total running time: 9min 31s
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 55/136[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 56/136[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3205)[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=3205)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
[1m119/169[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
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[36m(train_cnn_ray_tune pid=3205)[0m 
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step

Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:25:52. Total running time: 9min 42s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             579.209 │
│ time_total_s                 579.209 │
│ training_iteration                 1 │
│ val_accuracy                 0.38575 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:25:52. Total running time: 9min 42s
[36m(train_cnn_ray_tune pid=3191)[0m 
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 18ms/step - accuracy: 0.3574 - loss: 1.7903 
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[36m(train_cnn_ray_tune pid=3191)[0m 
[1m170/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m7s[0m 17ms/step - accuracy: 0.3550 - loss: 1.7746
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[36m(train_cnn_ray_tune pid=3188)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 389ms/step
[1m 8/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step   
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[36m(train_cnn_ray_tune pid=3188)[0m 
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[1m 10/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step 
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 57/136[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[1m 95/292[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step - accuracy: 0.1371 - loss: 2.7591[32m [repeated 274x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:25:54. Total running time: 9min 44s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              581.68 │
│ time_total_s                  581.68 │
│ training_iteration                 1 │
│ val_accuracy                 0.22775 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:25:54. Total running time: 9min 44s
[36m(train_cnn_ray_tune pid=3188)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 28ms/step - accuracy: 0.1404 - loss: 2.5806 - val_accuracy: 0.1465 - val_loss: 2.4589[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3188)[0m 
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3184)[0m 
[1m  7/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 17ms/step - accuracy: 0.2204 - loss: 2.0607 
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[36m(train_cnn_ray_tune pid=3191)[0m 
[1m538/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 15ms/step - accuracy: 0.3555 - loss: 1.7605
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[1m546/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 15ms/step - accuracy: 0.3554 - loss: 1.7604[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 58/136[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
[1m  7/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.1615 - loss: 2.4830  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 77ms/step - accuracy: 0.0625 - loss: 2.6603
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[36m(train_cnn_ray_tune pid=3185)[0m Epoch 32/57[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
[1m130/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 14ms/step - accuracy: 0.1233 - loss: 2.7867
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 33/75[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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Trial status: 12 TERMINATED | 8 RUNNING
Current time: 2025-11-05 20:26:10. Total running time: 10min 1s
Logical resource usage: 8.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              4   adam            relu                                   32                 64                  5          0.00487194          59                                              │
│ trial_e2c7c    RUNNING              3   adam            relu                                   64                 32                  3          0.000100792        136                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              2   adam            relu                                   32                128                  5          0.000868639        125                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    RUNNING              2   rmsprop         relu                                   64                128                  3          1.33251e-05         58                                              │
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                   32                128                  5          0.00409611         115        1            568.662         0.163552 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   32                128                  3          0.00280181         120        1            560.751         0.3321   │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   64                128                  5          0.000897052         52        1            579.209         0.385754 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                  128                 64                  3          0.000533151        148        1            581.68          0.227752 │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                128                  3          0.00353537          53        1            535.643         0.308418 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 71ms/step - accuracy: 0.2812 - loss: 1.9631[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3203)[0m Epoch 21/143[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m Epoch 63/136[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 35/75[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3197)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 241ms/step
[1m12/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step   
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
[1m112/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m4s[0m 11ms/step - accuracy: 0.1668 - loss: 2.4574
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[36m(train_cnn_ray_tune pid=3197)[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=3197)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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[36m(train_cnn_ray_tune pid=3197)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:26:25. Total running time: 10min 16s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             612.939 │
│ time_total_s                 612.939 │
│ training_iteration                 1 │
│ val_accuracy                 0.18705 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:26:25. Total running time: 10min 16s
[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3187)[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=3187)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:26:28. Total running time: 10min 18s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             615.904 │
│ time_total_s                 615.904 │
│ training_iteration                 1 │
│ val_accuracy                 0.23145 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:26:28. Total running time: 10min 18s
[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3187)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m Epoch 36/57[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3191)[0m 
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[36m(train_cnn_ray_tune pid=3184)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:26:30. Total running time: 10min 20s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             617.684 │
│ time_total_s                 617.684 │
│ training_iteration                 1 │
│ val_accuracy                 0.22368 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:26:30. Total running time: 10min 20s

Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:26:31. Total running time: 10min 21s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             618.385 │
│ time_total_s                 618.385 │
│ training_iteration                 1 │
│ val_accuracy                 0.39019 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:26:31. Total running time: 10min 21s
[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 46/128[32m [repeated 6x across cluster][0m
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 47/128[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-05 20:26:40. Total running time: 10min 31s
Logical resource usage: 4.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.00133012          75                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           4   adam            relu                                   32                 64                  5          0.00487194          59        1            615.904         0.231452 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                 32                  3          0.000100792        136        1            617.684         0.223682 │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                   32                128                  5          0.00409611         115        1            568.662         0.163552 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   32                128                  5          0.000868639        125        1            618.385         0.390194 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   32                128                  3          0.00280181         120        1            560.751         0.3321   │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   64                128                  5          0.000897052         52        1            579.209         0.385754 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                  128                 64                  3          0.000533151        148        1            581.68          0.227752 │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                128                  3          0.00353537          53        1            535.643         0.308418 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   64                128                  3          1.33251e-05         58        1            612.939         0.187049 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m Epoch 40/75[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 42ms/step - accuracy: 0.3125 - loss: 1.9604[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m Epoch 42/57[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3190)[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=3190)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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[36m(train_cnn_ray_tune pid=3190)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:26:59. Total running time: 10min 49s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             646.739 │
│ time_total_s                 646.739 │
│ training_iteration                 1 │
│ val_accuracy                 0.16485 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:26:59. Total running time: 10min 49s
[36m(train_cnn_ray_tune pid=3190)[0m 
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 55/128[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 8ms/step - accuracy: 0.1869 - loss: 2.3873 - val_accuracy: 0.2261 - val_loss: 2.2061[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m Epoch 57/128[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[1m 15/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 8ms/step - accuracy: 0.2943 - loss: 2.0541[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 8ms/step - accuracy: 0.1900 - loss: 2.3477 - val_accuracy: 0.2387 - val_loss: 2.1454[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m Epoch 59/128[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 41ms/step - accuracy: 0.1719 - loss: 2.3704[32m [repeated 4x across cluster][0m

Trial status: 17 TERMINATED | 3 RUNNING
Current time: 2025-11-05 20:27:10. Total running time: 11min 1s
Logical resource usage: 3.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              4   adam            tanh                                   64                128                  3          8.5657e-05          57                                              │
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    RUNNING              4   rmsprop         relu                                   32                128                  5          0.000327411        143                                              │
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           4   adam            relu                                   32                 64                  5          0.00487194          59        1            615.904         0.231452 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                 32                  3          0.000100792        136        1            617.684         0.223682 │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                   32                128                  5          0.00409611         115        1            568.662         0.163552 │
│ trial_e2c7c    TERMINATED           3   rmsprop         tanh                                   32                 32                  5          0.00133012          75        1            646.739         0.164847 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   32                128                  5          0.000868639        125        1            618.385         0.390194 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   32                128                  3          0.00280181         120        1            560.751         0.3321   │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   64                128                  5          0.000897052         52        1            579.209         0.385754 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                  128                 64                  3          0.000533151        148        1            581.68          0.227752 │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                128                  3          0.00353537          53        1            535.643         0.308418 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   64                128                  3          1.33251e-05         58        1            612.939         0.187049 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3200)[0m 
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.1987 - loss: 2.3014
[1m126/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.1989 - loss: 2.3012[32m [repeated 113x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3203)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 65/128[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=3203)[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=3203)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 67/128[32m [repeated 5x across cluster][0m
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:27:33. Total running time: 11min 23s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             680.826 │
│ time_total_s                 680.826 │
│ training_iteration                 1 │
│ val_accuracy                 0.29195 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:27:33. Total running time: 11min 23s
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3185)[0m Epoch 57/57[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3185)[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=3185)[0m   _log_deprecation_warning(
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:27:36. Total running time: 11min 27s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             683.802 │
│ time_total_s                 683.802 │
│ training_iteration                 1 │
│ val_accuracy                 0.24662 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:27:36. Total running time: 11min 27s
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[36m(train_cnn_ray_tune pid=3185)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 73/128[32m [repeated 4x across cluster][0m

Trial status: 19 TERMINATED | 1 RUNNING
Current time: 2025-11-05 20:27:40. Total running time: 11min 31s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    RUNNING              3   adam            tanh                                   64                128                  3          5.91221e-05        128                                              │
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                   64                128                  3          8.5657e-05          57        1            683.802         0.246623 │
│ trial_e2c7c    TERMINATED           4   adam            relu                                   32                 64                  5          0.00487194          59        1            615.904         0.231452 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                 32                  3          0.000100792        136        1            617.684         0.223682 │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                   32                128                  5          0.00409611         115        1            568.662         0.163552 │
│ trial_e2c7c    TERMINATED           3   rmsprop         tanh                                   32                 32                  5          0.00133012          75        1            646.739         0.164847 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   32                128                  5          0.000868639        125        1            618.385         0.390194 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   32                128                  3          0.00280181         120        1            560.751         0.3321   │
│ trial_e2c7c    TERMINATED           4   rmsprop         relu                                   32                128                  5          0.000327411        143        1            680.826         0.291952 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   64                128                  5          0.000897052         52        1            579.209         0.385754 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                  128                 64                  3          0.000533151        148        1            581.68          0.227752 │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                128                  3          0.00353537          53        1            535.643         0.308418 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   64                128                  3          1.33251e-05         58        1            612.939         0.187049 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 80/128[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 84/128[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 88/128[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m 
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2025-11-05 20:28:09,472	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_ESANN_acc_17_classes/ESANN_hyperparameters_tuning' in 0.0084s.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762370889.615063    1414 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=3200)[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=3200)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3200)[0m 
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Trial trial_e2c7c finished iteration 1 at 2025-11-05 20:28:09. Total running time: 11min 59s
╭──────────────────────────────────────╮
│ Trial trial_e2c7c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             716.475 │
│ time_total_s                 716.475 │
│ training_iteration                 1 │
│ val_accuracy                 0.28048 │
╰──────────────────────────────────────╯

Trial trial_e2c7c completed after 1 iterations at 2025-11-05 20:28:09. Total running time: 11min 59s

Trial status: 20 TERMINATED
Current time: 2025-11-05 20:28:09. Total running time: 11min 59s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_e2c7c    TERMINATED           4   adam            relu                                  128                128                  3          0.000576404         77        1            476.71          0.248844 │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                   64                128                  3          8.5657e-05          57        1            683.802         0.246623 │
│ trial_e2c7c    TERMINATED           4   adam            relu                                   32                 64                  5          0.00487194          59        1            615.904         0.231452 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                  128                 32                  3          0.000611667        145        1            267.03          0.275671 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                 32                  3          0.000100792        136        1            617.684         0.223682 │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                   32                128                  5          0.00409611         115        1            568.662         0.163552 │
│ trial_e2c7c    TERMINATED           3   rmsprop         tanh                                   32                 32                  5          0.00133012          75        1            646.739         0.164847 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   32                128                  5          0.000868639        125        1            618.385         0.390194 │
│ trial_e2c7c    TERMINATED           3   adam            tanh                                   64                128                  3          5.91221e-05        128        1            716.475         0.280481 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   32                128                  3          0.00280181         120        1            560.751         0.3321   │
│ trial_e2c7c    TERMINATED           4   rmsprop         relu                                   32                128                  5          0.000327411        143        1            680.826         0.291952 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                  128                 64                  5          0.000211066         57        1            452.634         0.252729 │
│ trial_e2c7c    TERMINATED           2   adam            relu                                   64                128                  5          0.000897052         52        1            579.209         0.385754 │
│ trial_e2c7c    TERMINATED           3   rmsprop         relu                                   32                128                  5          1.46437e-05         60        1            285.961         0.13284  │
│ trial_e2c7c    TERMINATED           4   adam            tanh                                  128                 64                  3          0.000533151        148        1            581.68          0.227752 │
│ trial_e2c7c    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00419526          96        1            432.145         0.241443 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                  128                 32                  5          0.000166185         53        1            384.439         0.225532 │
│ trial_e2c7c    TERMINATED           3   adam            relu                                   64                128                  3          0.00353537          53        1            535.643         0.308418 │
│ trial_e2c7c    TERMINATED           4   rmsprop         tanh                                   32                128                  3          0.00492245         141        1            324.274         0.140796 │
│ trial_e2c7c    TERMINATED           2   rmsprop         relu                                   64                128                  3          1.33251e-05         58        1            612.939         0.187049 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 32, 'numero_filtros': 128, 'tamanho_filtro': 5, 'tasa_aprendizaje': 0.0008686386062061349, 'epochs': 125}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762370891.548592   90603 service.cc:152] XLA service 0x73b17401da90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762370891.548637   90603 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:28:11.597006: 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 00:00:1762370891.770954   90603 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762370894.204710   90603 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 2/125

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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.6123
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.5261
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Epoch 5/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1864 - loss: 2.3000 - val_accuracy: 0.2296 - val_loss: 2.1343
Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.9582
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2334 - loss: 2.1521  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.1713
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[1m344/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2181 - loss: 2.1914
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.1250 - loss: 2.1504
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2152 - loss: 2.1773  
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2193 - loss: 2.1642
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8385
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2548 - loss: 2.0606  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2388 - loss: 2.1004 - val_accuracy: 0.2620 - val_loss: 1.9598
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1279
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2356 - loss: 2.0622  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.0595
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[1m150/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2491 - loss: 2.0569
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[1m303/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2509 - loss: 2.0519
[1m336/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2510 - loss: 2.0518
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2512 - loss: 2.0516 - val_accuracy: 0.2995 - val_loss: 1.9184
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 1.9383
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2530 - loss: 2.0243  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0173
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[1m160/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 2.0167
[1m201/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2568 - loss: 2.0178
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.8826
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0277  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2593 - loss: 2.0164
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2738 - loss: 1.9925 - val_accuracy: 0.3056 - val_loss: 1.8786
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.3484
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0595  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2631 - loss: 2.0178
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[1m150/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 1.9868
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[1m260/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2751 - loss: 1.9747
[1m298/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2758 - loss: 1.9729
[1m338/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2764 - loss: 1.9711
[1m378/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2768 - loss: 1.9698
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2780 - loss: 1.9649 - val_accuracy: 0.3105 - val_loss: 1.8450
Epoch 13/125

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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8306
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2862 - loss: 1.9129  
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Epoch 16/125

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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 24ms/step - accuracy: 0.2812 - loss: 1.9043
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3185 - loss: 1.8344  
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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9625
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3062 - loss: 1.8856  
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Epoch 19/125

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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.6721
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3076 - loss: 1.8296  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3066 - loss: 1.8446
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.7508
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.7640  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3387 - loss: 1.7983 - val_accuracy: 0.3652 - val_loss: 1.7458
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.6949
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.7289  
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[1m582/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3367 - loss: 1.7937
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3368 - loss: 1.7937 - val_accuracy: 0.3796 - val_loss: 1.7153
Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4375 - loss: 1.5753
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3747 - loss: 1.6852  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3618 - loss: 1.7262
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[1m233/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7590
[1m274/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7612
[1m310/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7628
[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7642
[1m389/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7658
[1m429/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7673
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[1m508/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7695
[1m547/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7702
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3443 - loss: 1.7710 - val_accuracy: 0.3802 - val_loss: 1.7065
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9835
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3275 - loss: 1.7992  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.7944
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Epoch 25/125

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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.6690
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.7204  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.7429
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7240
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.6620  
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3563 - loss: 1.7129
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.6974
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3125 - loss: 1.7840
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3689 - loss: 1.7729  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3703 - loss: 1.7546
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Epoch 30/125

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Epoch 31/125

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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.5625
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3809 - loss: 1.6962  
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Epoch 33/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3657 - loss: 1.6944  
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5000 - loss: 1.4152
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3912 - loss: 1.6778  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3855 - loss: 1.6767
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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.5726
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3935 - loss: 1.6252  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3861 - loss: 1.6732 - val_accuracy: 0.3974 - val_loss: 1.6580
Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7382
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.5847  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.5973
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3750 - loss: 1.6353
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3692 - loss: 1.6847  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3751 - loss: 1.6823
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[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3826 - loss: 1.6611
[1m344/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3834 - loss: 1.6600
[1m384/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3838 - loss: 1.6594
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[1m580/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.6593
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7251
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3759 - loss: 1.6690  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3807 - loss: 1.6532
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Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9078
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3780 - loss: 1.7359  
[1m 70/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3827 - loss: 1.7208
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Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 29ms/step - accuracy: 0.4375 - loss: 1.6172
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3872 - loss: 1.6439  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3856 - loss: 1.6448
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[1m306/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3862 - loss: 1.6484
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[1m382/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3871 - loss: 1.6481
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[1m495/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3881 - loss: 1.6482
[1m531/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3884 - loss: 1.6480
[1m570/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3887 - loss: 1.6480
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Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3750 - loss: 1.7732
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3741 - loss: 1.6730  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3760 - loss: 1.6742
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Epoch 42/125

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Epoch 43/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9702
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4303 - loss: 1.5571  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4058 - loss: 1.6003
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[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3941 - loss: 1.6383
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Epoch 44/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.4466
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3791 - loss: 1.6291  
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Epoch 45/125

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Epoch 46/125

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Epoch 47/125

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Epoch 48/125

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Epoch 49/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4198 - loss: 1.5715  
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Saved model to disk.
[36m(train_cnn_ray_tune pid=3200)[0m 
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[36m(train_cnn_ray_tune pid=3200)[0m Epoch 94/128[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3200)[0m 
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=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 752us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 68/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 748us/step
[1m141/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 716us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.69 [%]
Global F1 score (validation) = 37.81 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.73635066e-01 1.05833605e-01 2.18062118e-01 ... 5.82544288e-08
  3.12157363e-01 1.81797873e-02]
 [1.87026560e-01 2.64422387e-01 2.99230635e-01 ... 3.19323419e-07
  8.11859965e-02 2.53221067e-03]
 [1.70550555e-01 3.63663018e-01 2.65801340e-01 ... 5.82911025e-06
  3.33245881e-02 1.49877300e-03]
 ...
 [1.40328854e-01 1.70574382e-01 1.91749021e-01 ... 3.01999108e-07
  3.63294184e-01 1.43294018e-02]
 [1.08417310e-01 1.30402178e-01 1.41543552e-01 ... 8.98796692e-03
  2.56230950e-01 4.14836407e-02]
 [1.09530710e-01 1.81896418e-01 1.62251621e-01 ... 1.12313021e-04
  4.09466922e-01 2.30447520e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 51.01 [%]
Global accuracy score (test) = 36.83 [%]
Global F1 score (train) = 48.29 [%]
Global F1 score (test) = 34.36 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       1.00      0.01      0.02       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.47      0.34       184
       CAMINAR USUAL SPEED       0.24      0.23      0.24       184
            CAMINAR ZIGZAG       0.18      0.14      0.16       184
          DE PIE BARRIENDO       0.40      0.35      0.37       184
   DE PIE DOBLANDO TOALLAS       0.36      0.38      0.37       184
    DE PIE MOVIENDO LIBROS       0.32      0.15      0.21       184
          DE PIE USANDO PC       0.34      0.60      0.43       184
        FASE REPOSO CON K5       0.33      0.75      0.46       184
INCREMENTAL CICLOERGOMETRO       0.91      0.47      0.62       184
           SENTADO LEYENDO       0.32      0.61      0.42       184
         SENTADO USANDO PC       0.27      0.22      0.24       184
      SENTADO VIENDO LA TV       0.00      0.00      0.00       184
   SUBIR Y BAJAR ESCALERAS       0.50      0.51      0.50       184
                    TROTAR       0.92      0.66      0.77       161

                  accuracy                           0.37      2737
                 macro avg       0.42      0.37      0.34      2737
              weighted avg       0.42      0.37      0.34      2737

2025-11-05 20:29:23.124820: 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`.
2025-11-05 20:29:23.136458: 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 00:00:1762370963.150723   96445 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762370963.154923   96445 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762370963.165543   96445 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762370963.165566   96445 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762370963.165568   96445 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762370963.165569   96445 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:29:23.168873: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762370965.436415   96445 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762370967.096377   96578 service.cc:152] XLA service 0x74f92401dbf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762370967.096402   96578 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:29:27.129438: 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 00:00:1762370967.294262   96578 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762370969.702001   96578 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:02[0m 4s/step - accuracy: 0.0312 - loss: 4.3217
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0762 - loss: 4.2324  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0773 - loss: 4.1874
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Epoch 2/125

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Epoch 3/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1621 - loss: 2.5599  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1585 - loss: 2.5563
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Epoch 4/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1605 - loss: 2.4265  
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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0938 - loss: 2.1907
[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1740 - loss: 2.2746  
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 2.2816
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2103 - loss: 2.1847  
[1m 68/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2068 - loss: 2.1850
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.9448
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2464 - loss: 2.0901  
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[1m193/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2340 - loss: 2.1359
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[1m346/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2303 - loss: 2.1388
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[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2294 - loss: 2.1353
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2294 - loss: 2.1352 - val_accuracy: 0.2670 - val_loss: 1.9795
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9796
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2474 - loss: 2.0646  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2460 - loss: 2.0774
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[1m391/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2396 - loss: 2.0883
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2418 - loss: 2.0827 - val_accuracy: 0.2725 - val_loss: 1.9485
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.0956
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2451 - loss: 2.0528  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2477 - loss: 2.0616
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Epoch 10/125

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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.2500 - loss: 1.9470
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2828 - loss: 1.9507  
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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9677
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3025 - loss: 1.9011  
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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1120
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2793 - loss: 1.9770  
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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 1.8755
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2881 - loss: 1.9409  
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[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2951 - loss: 1.9155
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2951 - loss: 1.9155 - val_accuracy: 0.3393 - val_loss: 1.8060
Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 2.0652
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2959 - loss: 1.9597  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2908 - loss: 1.9457
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Epoch 16/125

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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.6044
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Epoch 18/125

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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1378
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3062 - loss: 1.8898  
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 24ms/step - accuracy: 0.3750 - loss: 1.8462
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3272 - loss: 1.8159  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3257 - loss: 1.8129
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.4375 - loss: 1.5842
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3523 - loss: 1.7526  
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 26ms/step - accuracy: 0.2500 - loss: 1.9991
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.7398
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3104 - loss: 1.7934  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.7951
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.6361
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7540  
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Epoch 25/125

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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6517
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.7138  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3550 - loss: 1.7413
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.7167
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4375 - loss: 1.7488
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3884 - loss: 1.7511  
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5938 - loss: 1.7166
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3869 - loss: 1.6560  
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Epoch 31/125

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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.4292
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4131 - loss: 1.6122  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3933 - loss: 1.6500
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[1m192/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3840 - loss: 1.6711
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.4349
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4066 - loss: 1.6437  
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[1m194/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3908 - loss: 1.6723
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[1m271/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3882 - loss: 1.6717
[1m312/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3871 - loss: 1.6720
[1m352/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3861 - loss: 1.6720
[1m392/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3850 - loss: 1.6726
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[1m547/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3826 - loss: 1.6741
[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3821 - loss: 1.6747
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3821 - loss: 1.6748 - val_accuracy: 0.3948 - val_loss: 1.6508
Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4688 - loss: 1.5367
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.6710
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[1m506/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3789 - loss: 1.6841
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3788 - loss: 1.6845 - val_accuracy: 0.3954 - val_loss: 1.6630
Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7120
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4037 - loss: 1.6454  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4009 - loss: 1.6566
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3936 - loss: 1.6630
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[1m239/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3890 - loss: 1.6665
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[1m319/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3865 - loss: 1.6683
[1m357/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3856 - loss: 1.6694
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 570ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 822us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 1: 36.83 [%]
F1-score capturado en la ejecución 1: 34.36 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 70/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 729us/step
[1m141/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 717us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.52 [%]
Global F1 score (validation) = 35.09 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[8.4349662e-02 1.0462673e-01 1.0634445e-01 ... 1.9945512e-05
  5.5418420e-01 2.2507574e-02]
 [1.3374454e-01 1.7425816e-01 1.7218398e-01 ... 4.2570351e-05
  3.2069653e-01 2.0991893e-02]
 [1.5512033e-01 2.3530261e-01 1.9472441e-01 ... 1.3414056e-04
  1.6810143e-01 1.8838389e-02]
 ...
 [1.4793199e-01 2.4533311e-01 2.0455767e-01 ... 6.9465136e-07
  2.2750697e-01 7.1180626e-03]
 [1.0194021e-01 1.4768547e-01 1.3004237e-01 ... 2.3373326e-04
  4.3151081e-01 3.8719408e-02]
 [8.9343853e-02 1.8538992e-01 1.3213667e-01 ... 2.5726136e-05
  4.6042049e-01 1.3691818e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 46.46 [%]
Global accuracy score (test) = 35.92 [%]
Global F1 score (train) = 43.11 [%]
Global F1 score (test) = 33.32 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.03      0.05       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.54      0.35       184
       CAMINAR USUAL SPEED       0.67      0.01      0.02       184
            CAMINAR ZIGZAG       0.23      0.18      0.20       184
          DE PIE BARRIENDO       0.21      0.27      0.24       184
   DE PIE DOBLANDO TOALLAS       0.37      0.39      0.38       184
    DE PIE MOVIENDO LIBROS       0.33      0.09      0.14       184
          DE PIE USANDO PC       0.34      0.60      0.43       184
        FASE REPOSO CON K5       0.43      0.75      0.55       184
INCREMENTAL CICLOERGOMETRO       0.63      0.52      0.57       184
           SENTADO LEYENDO       0.34      0.36      0.35       184
         SENTADO USANDO PC       0.21      0.22      0.21       184
      SENTADO VIENDO LA TV       0.42      0.25      0.31       184
   SUBIR Y BAJAR ESCALERAS       0.37      0.64      0.47       184
                    TROTAR       0.97      0.57      0.72       161

                  accuracy                           0.36      2737
                 macro avg       0.40      0.36      0.33      2737
              weighted avg       0.40      0.36      0.33      2737

2025-11-05 20:30:22.216062: 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`.
2025-11-05 20:30:22.227518: 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 00:00:1762371022.240645  101008 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371022.244704  101008 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371022.254457  101008 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371022.254473  101008 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371022.254475  101008 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371022.254476  101008 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:30:22.257612: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371024.511869  101008 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371026.153230  101134 service.cc:152] XLA service 0x7583b000cfd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371026.153254  101134 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:30:26.188719: 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 00:00:1762371026.352926  101134 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371028.752459  101134 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34:49[0m 4s/step - accuracy: 0.0312 - loss: 4.6320
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0772 - loss: 4.3479  
[1m 72/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0798 - loss: 4.2969
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[1m226/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0861 - loss: 4.0441
[1m266/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0870 - loss: 3.9856
[1m303/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0880 - loss: 3.9339
[1m344/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0891 - loss: 3.8801
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Epoch 2/125

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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4606
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1519 - loss: 2.5198  
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 2.3533
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Epoch 5/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1999 - loss: 2.2698 - val_accuracy: 0.2183 - val_loss: 2.1072
Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1508
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2317 - loss: 2.1813  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.1830
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.1732
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1250 - loss: 2.3600
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2540 - loss: 2.0911  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2411 - loss: 2.0919 - val_accuracy: 0.2666 - val_loss: 1.9672
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.8244
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2401 - loss: 1.9851  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2485 - loss: 2.0072
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[1m140/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2474 - loss: 2.0341
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[1m253/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2481 - loss: 2.0450
[1m292/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.0463
[1m329/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2490 - loss: 2.0470
[1m366/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2490 - loss: 2.0477
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.0432
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2554 - loss: 2.0308  
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[1m190/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0248
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0951
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[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2525 - loss: 2.0482
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2555 - loss: 2.0359
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[1m399/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0174
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2634 - loss: 2.0093 - val_accuracy: 0.3130 - val_loss: 1.8634
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.7503
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2920 - loss: 1.9599  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2804 - loss: 1.9684
[1m119/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2755 - loss: 1.9746
[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2724 - loss: 1.9768
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[1m234/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2702 - loss: 1.9787
[1m274/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2701 - loss: 1.9781
[1m314/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2702 - loss: 1.9775
[1m352/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2705 - loss: 1.9773
[1m391/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2708 - loss: 1.9770
[1m433/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2711 - loss: 1.9765
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[1m511/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 1.9753
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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.9711
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 1.9131  
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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 1.9628
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2832 - loss: 1.9116  
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Epoch 16/125

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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9252
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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 1.8777
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.8781  
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7634
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3130 - loss: 1.8641  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3153 - loss: 1.8609
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3192 - loss: 1.8441 - val_accuracy: 0.3528 - val_loss: 1.7567
Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.8136
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3414 - loss: 1.8606  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.8333
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3750 - loss: 1.8079
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7779  
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8936
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 1.8288  
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8770
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7947  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3368 - loss: 1.7983
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[1m514/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3376 - loss: 1.7920
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3377 - loss: 1.7921 - val_accuracy: 0.3673 - val_loss: 1.7207
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.6066
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3127 - loss: 1.7908  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3248 - loss: 1.7886
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3750 - loss: 1.7529
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.5865
[1m 31/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.7428  
[1m 67/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3453 - loss: 1.7466
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.6824
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3642 - loss: 1.7060  
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.0809
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.9158
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3164 - loss: 1.7634  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7357
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7937
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3668 - loss: 1.6880  
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Epoch 31/125

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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7426
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4247 - loss: 1.6929  
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.6482
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3677 - loss: 1.6779  
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Epoch 34/125

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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8262
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.6891  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.6992
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6887
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3812 - loss: 1.6680  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3803 - loss: 1.6591
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[1m270/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3825 - loss: 1.6613
[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3821 - loss: 1.6622
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.4590
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.7340  
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[1m570/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3822 - loss: 1.6781
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3822 - loss: 1.6779 - val_accuracy: 0.3802 - val_loss: 1.6312
Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5938 - loss: 1.3312
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.5922  
[1m 72/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.6134
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[1m189/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4061 - loss: 1.6289
[1m228/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4028 - loss: 1.6337
[1m267/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4003 - loss: 1.6376
[1m300/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3986 - loss: 1.6396
[1m339/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3971 - loss: 1.6421
[1m379/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3956 - loss: 1.6443
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[1m533/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3922 - loss: 1.6505
[1m571/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3915 - loss: 1.6518
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Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7351
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4096 - loss: 1.6013  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4070 - loss: 1.6087
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Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8677
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3798 - loss: 1.6942  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3920 - loss: 1.6600 - val_accuracy: 0.3858 - val_loss: 1.6612
Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7811
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.7363  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3725 - loss: 1.6994
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Epoch 42/125

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Epoch 43/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.3750 - loss: 1.6118
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4026 - loss: 1.6451  
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Epoch 44/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3438 - loss: 1.4911
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3840 - loss: 1.6055  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3837 - loss: 1.6178
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 2: 35.92 [%]
F1-score capturado en la ejecución 2: 33.32 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 62/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 821us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 756us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.54 [%]
Global F1 score (validation) = 37.75 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.1435073e-01 6.1829776e-02 1.3156049e-01 ... 1.3438152e-07
  5.4184538e-01 4.0503755e-02]
 [2.4911094e-01 2.8528777e-01 2.6035717e-01 ... 6.3307034e-08
  1.3204593e-02 7.0285663e-04]
 [1.6509441e-01 2.0703219e-01 1.8388486e-01 ... 1.8843313e-03
  1.3435474e-01 1.9416062e-02]
 ...
 [1.7345099e-01 1.4051896e-01 1.9934748e-01 ... 6.4231963e-06
  3.4863770e-01 1.7628832e-02]
 [1.3687384e-01 1.2017297e-01 1.4238414e-01 ... 3.4877269e-03
  3.4038115e-01 4.4523574e-02]
 [1.3584453e-01 1.5055418e-01 1.5826590e-01 ... 4.5338034e-04
  3.2883680e-01 3.4659345e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.03 [%]
Global accuracy score (test) = 35.84 [%]
Global F1 score (train) = 49.5 [%]
Global F1 score (test) = 34.76 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.28      0.26       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.28      0.25       184
       CAMINAR USUAL SPEED       0.21      0.11      0.14       184
            CAMINAR ZIGZAG       0.04      0.01      0.02       184
          DE PIE BARRIENDO       0.27      0.21      0.24       184
   DE PIE DOBLANDO TOALLAS       0.35      0.45      0.39       184
    DE PIE MOVIENDO LIBROS       0.28      0.11      0.16       184
          DE PIE USANDO PC       0.38      0.60      0.47       184
        FASE REPOSO CON K5       0.35      0.74      0.47       184
INCREMENTAL CICLOERGOMETRO       0.85      0.46      0.60       184
           SENTADO LEYENDO       0.36      0.36      0.36       184
         SENTADO USANDO PC       0.22      0.34      0.26       184
      SENTADO VIENDO LA TV       0.57      0.25      0.35       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.58      0.49       184
                    TROTAR       0.94      0.63      0.75       161

                  accuracy                           0.36      2737
                 macro avg       0.38      0.36      0.35      2737
              weighted avg       0.38      0.36      0.34      2737

2025-11-05 20:31:30.400210: 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`.
2025-11-05 20:31:30.411391: 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 00:00:1762371090.425118  106465 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371090.429075  106465 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371090.439189  106465 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371090.439208  106465 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371090.439209  106465 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371090.439211  106465 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:31:30.442333: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371092.690116  106465 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371094.391171  106572 service.cc:152] XLA service 0x7c75bc00ca10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371094.391198  106572 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:31:34.424727: 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 00:00:1762371094.603409  106572 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371097.068404  106572 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:48[0m 4s/step - accuracy: 0.0312 - loss: 5.0026
[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0693 - loss: 4.5779  
[1m 72/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0771 - loss: 4.4649
[1m113/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0809 - loss: 4.3819
[1m152/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0841 - loss: 4.3070
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Epoch 2/125

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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1250 - loss: 2.5009
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.1875 - loss: 2.6080
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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3678
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1250 - loss: 2.3587
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2102 - loss: 2.1821  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2138 - loss: 2.1841
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[1m156/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2143 - loss: 2.1949
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[1m235/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2151 - loss: 2.1988
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[1m310/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2141 - loss: 2.2006
[1m346/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2136 - loss: 2.2012
[1m384/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.2015
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Epoch 7/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2270 - loss: 2.1287  
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[1m230/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2191 - loss: 2.1502
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Epoch 8/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2387 - loss: 2.0958 - val_accuracy: 0.2675 - val_loss: 1.9578
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.9556
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2608 - loss: 2.0498  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2555 - loss: 2.0578
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[1m158/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0647
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[1m274/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0664
[1m313/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2476 - loss: 2.0670
[1m351/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2473 - loss: 2.0670
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2478 - loss: 2.0635
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2478 - loss: 2.0635 - val_accuracy: 0.2844 - val_loss: 1.9116
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3750 - loss: 1.8579
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2581 - loss: 2.0142  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2528 - loss: 2.0190
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[1m579/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2578 - loss: 2.0185
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2578 - loss: 2.0185 - val_accuracy: 0.2801 - val_loss: 1.9418
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 27ms/step - accuracy: 0.2188 - loss: 2.1003
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0457  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2564 - loss: 2.0165
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[1m508/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2659 - loss: 1.9973
[1m548/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2662 - loss: 1.9966
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2664 - loss: 1.9959 - val_accuracy: 0.3275 - val_loss: 1.8479
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.0957
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2360 - loss: 2.0270  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2473 - loss: 2.0199
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2630 - loss: 1.9978
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2724 - loss: 1.9771 - val_accuracy: 0.3005 - val_loss: 1.8824
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.2003
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[1m 65/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2884 - loss: 1.9586
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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.8694
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3207 - loss: 1.8256  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3098 - loss: 1.8482
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[1m535/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2996 - loss: 1.8891
[1m572/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2995 - loss: 1.8903
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2995 - loss: 1.8906 - val_accuracy: 0.3384 - val_loss: 1.7910
Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7782
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2901 - loss: 1.8830  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2895 - loss: 1.8950
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Epoch 16/125

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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.9341
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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7634
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3258 - loss: 1.8538 - val_accuracy: 0.3769 - val_loss: 1.7366
Epoch 19/125

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[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.8327  
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.7105
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3527 - loss: 1.7377  
[1m 83/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7664
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[1m547/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.8193
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.8190
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2622
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.8951  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3354 - loss: 1.8584
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3372 - loss: 1.8173 - val_accuracy: 0.3613 - val_loss: 1.7241
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.5138
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7674  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7721
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[1m574/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.7823
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3433 - loss: 1.7824 - val_accuracy: 0.3682 - val_loss: 1.7039
Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8505
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.8188  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3313 - loss: 1.7986
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[1m316/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7778
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[1m391/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7776
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[1m543/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7785
[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7785
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3457 - loss: 1.7784 - val_accuracy: 0.3895 - val_loss: 1.6819
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7329
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7333  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.7422
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[1m231/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.7511
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.7557
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7798
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3694 - loss: 1.6720  
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.7369
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7903  
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Epoch 28/125

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Epoch 29/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3648 - loss: 1.7122  
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Epoch 30/125

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Epoch 31/125

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Saved model to disk.

Accuracy capturado en la ejecución 3: 35.84 [%]
F1-score capturado en la ejecución 3: 34.76 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 66/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 772us/step
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 765us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.15 [%]
Global F1 score (validation) = 37.86 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.0256821e-01 8.2661219e-02 1.1474481e-01 ... 6.1089477e-06
  5.6618851e-01 1.5627058e-02]
 [2.1460809e-01 2.1338287e-01 2.4632043e-01 ... 3.4619109e-07
  1.0823334e-01 5.3616492e-03]
 [1.6800593e-01 2.5802571e-01 2.2631532e-01 ... 5.5904186e-04
  8.4375747e-02 1.5205996e-02]
 ...
 [1.5617523e-01 1.6282596e-01 1.8614951e-01 ... 1.0779908e-06
  3.4503457e-01 1.0078691e-02]
 [1.2225992e-01 1.8235266e-01 1.6702020e-01 ... 1.2266662e-03
  2.9065511e-01 2.0074518e-02]
 [1.1507681e-01 1.7397681e-01 1.4686275e-01 ... 4.1891675e-04
  3.7344021e-01 3.0180475e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.97 [%]
Global accuracy score (test) = 33.76 [%]
Global F1 score (train) = 47.07 [%]
Global F1 score (test) = 32.67 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.12      0.17       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.39      0.34       184
       CAMINAR USUAL SPEED       0.24      0.26      0.25       184
            CAMINAR ZIGZAG       0.23      0.08      0.12       184
          DE PIE BARRIENDO       0.27      0.31      0.29       184
   DE PIE DOBLANDO TOALLAS       0.42      0.29      0.34       184
    DE PIE MOVIENDO LIBROS       0.34      0.16      0.21       184
          DE PIE USANDO PC       0.40      0.61      0.48       184
        FASE REPOSO CON K5       0.28      0.77      0.41       184
INCREMENTAL CICLOERGOMETRO       0.85      0.47      0.60       184
           SENTADO LEYENDO       0.16      0.24      0.19       184
         SENTADO USANDO PC       0.23      0.22      0.22       184
      SENTADO VIENDO LA TV       0.00      0.00      0.00       184
   SUBIR Y BAJAR ESCALERAS       0.50      0.55      0.52       184
                    TROTAR       0.91      0.63      0.75       161

                  accuracy                           0.34      2737
                 macro avg       0.36      0.34      0.33      2737
              weighted avg       0.35      0.34      0.32      2737

2025-11-05 20:32:25.583657: 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`.
2025-11-05 20:32:25.595016: 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 00:00:1762371145.608309  110595 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371145.612213  110595 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371145.622279  110595 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371145.622296  110595 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371145.622298  110595 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371145.622299  110595 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:32:25.625433: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371147.910471  110595 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371149.543292  110733 service.cc:152] XLA service 0x73969400cbc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371149.543318  110733 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:32:29.576128: 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 00:00:1762371149.746675  110733 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371152.169882  110733 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:06[0m 4s/step - accuracy: 0.0625 - loss: 5.2303
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0751 - loss: 4.4983  
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[1m114/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0787 - loss: 4.3493
[1m148/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0798 - loss: 4.2764
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[1m225/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0826 - loss: 4.1209
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[1m300/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0850 - loss: 3.9914
[1m340/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0862 - loss: 3.9314
[1m379/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0873 - loss: 3.8775
[1m416/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0883 - loss: 3.8302
[1m457/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0894 - loss: 3.7823
[1m496/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0903 - loss: 3.7404
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[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.0922 - loss: 3.6626
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

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Epoch 6/125

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[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2127 - loss: 2.1986
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2127 - loss: 2.1986 - val_accuracy: 0.2581 - val_loss: 2.0571
Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.1562 - loss: 2.2036
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2092 - loss: 2.1742  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.1587
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[1m225/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2154 - loss: 2.1523
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[1m304/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2163 - loss: 2.1527
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Epoch 8/125

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[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2290 - loss: 2.1003  
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Epoch 9/125

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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.1562 - loss: 2.0011
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2394 - loss: 2.0598  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2475 - loss: 2.0499
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.2494
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2607 - loss: 2.0278  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2657 - loss: 2.0151
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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.3322
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2745 - loss: 2.0070  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 2.0047
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[1m511/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 1.9766
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2731 - loss: 1.9755 - val_accuracy: 0.3367 - val_loss: 1.8356
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9970
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2914 - loss: 1.9470  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2873 - loss: 1.9522
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Epoch 14/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2996 - loss: 1.9145  
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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.7148
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3074 - loss: 1.9142  
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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.8979
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3385 - loss: 1.8603  
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Epoch 17/125

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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.5895
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.7905  
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7459
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Epoch 20/125

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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8413
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3324 - loss: 1.7866  
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.5653
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.8049  
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Epoch 23/125

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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.1875 - loss: 2.0638
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3229 - loss: 1.8043  
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7928
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6783
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.7802  
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3750 - loss: 1.8326
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8966
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.7342  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.7329
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[1m314/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7569
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.9047
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7595  
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Epoch 29/125

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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7546
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3818 - loss: 1.7329  
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.6639
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7033  
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Epoch 32/125

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Epoch 33/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.7003  
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Epoch 34/125

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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8549
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3892 - loss: 1.6958  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3775 - loss: 1.7018 - val_accuracy: 0.3891 - val_loss: 1.6635
Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7789
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3707 - loss: 1.6695  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.6608
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Epoch 37/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.6572  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3823 - loss: 1.6662
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.6677
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3738 - loss: 1.6943  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3789 - loss: 1.6765 - val_accuracy: 0.3895 - val_loss: 1.6661
Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.8012
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.7343  
[1m 72/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7250
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Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7764
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3686 - loss: 1.6415  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3755 - loss: 1.6403
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Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4062 - loss: 1.7071
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4085 - loss: 1.6536  
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Epoch 42/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7025
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3899 - loss: 1.7092  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3864 - loss: 1.6991
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Epoch 43/125

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Epoch 44/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 1.6215
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Epoch 45/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.6254
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 565ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 885us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 4: 33.76 [%]
F1-score capturado en la ejecución 4: 32.67 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 62/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 826us/step
[1m128/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 795us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.59 [%]
Global F1 score (validation) = 38.41 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[4.5893036e-02 3.3175617e-02 6.1365638e-02 ... 8.8999548e-07
  8.0655688e-01 9.4658798e-03]
 [1.3666880e-01 1.5360643e-01 1.9402683e-01 ... 4.4764820e-06
  3.7255654e-01 9.3817851e-03]
 [1.7419477e-01 2.2858541e-01 2.2194639e-01 ... 4.0891337e-05
  1.6324177e-01 1.6663706e-02]
 ...
 [1.6553299e-01 1.7987217e-01 2.3241271e-01 ... 7.6640581e-06
  2.6351902e-01 1.0444384e-02]
 [1.3926432e-01 1.4214836e-01 1.8829505e-01 ... 9.4101168e-05
  3.7582096e-01 1.9956522e-02]
 [1.0705726e-01 1.1546440e-01 1.4279866e-01 ... 4.7549434e-05
  4.8146108e-01 3.2843176e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 49.18 [%]
Global accuracy score (test) = 36.68 [%]
Global F1 score (train) = 48.28 [%]
Global F1 score (test) = 35.77 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.02      0.04       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.23      0.24       184
       CAMINAR USUAL SPEED       0.22      0.38      0.28       184
            CAMINAR ZIGZAG       0.12      0.05      0.07       184
          DE PIE BARRIENDO       0.22      0.28      0.25       184
   DE PIE DOBLANDO TOALLAS       0.40      0.36      0.38       184
    DE PIE MOVIENDO LIBROS       0.36      0.29      0.32       184
          DE PIE USANDO PC       0.45      0.59      0.51       184
        FASE REPOSO CON K5       0.35      0.76      0.48       184
INCREMENTAL CICLOERGOMETRO       0.89      0.51      0.65       184
           SENTADO LEYENDO       0.29      0.26      0.27       184
         SENTADO USANDO PC       0.26      0.30      0.28       184
      SENTADO VIENDO LA TV       0.47      0.33      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.55      0.46       184
                    TROTAR       0.92      0.64      0.75       161

                  accuracy                           0.37      2737
                 macro avg       0.39      0.37      0.36      2737
              weighted avg       0.38      0.37      0.35      2737

2025-11-05 20:33:34.911428: 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`.
2025-11-05 20:33:34.923018: 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 00:00:1762371214.936788  116144 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371214.941032  116144 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371214.951329  116144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371214.951349  116144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371214.951350  116144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371214.951358  116144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:33:34.954586: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371217.251921  116144 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371218.907473  116277 service.cc:152] XLA service 0x791ea0014200 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371218.907507  116277 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:33:38.942353: 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 00:00:1762371219.107780  116277 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371221.585148  116277 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:42[0m 4s/step - accuracy: 0.0000e+00 - loss: 4.7919
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0701 - loss: 4.4068      
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0773 - loss: 4.2923
[1m109/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0800 - loss: 4.2074
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Epoch 2/125

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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.5963
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1652 - loss: 2.5117  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1554 - loss: 2.5218
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[1m318/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1491 - loss: 2.5196
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.1875 - loss: 2.4076
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Epoch 5/125

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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1738
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2072 - loss: 2.2447  
[1m 72/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2061 - loss: 2.2366
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[1m270/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2025 - loss: 2.2222
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[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2033 - loss: 2.2164
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Epoch 7/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2127 - loss: 2.1361  
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1875 - loss: 2.3242
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2735 - loss: 2.0974  
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[1m511/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2452 - loss: 2.0886
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2450 - loss: 2.0874 - val_accuracy: 0.2736 - val_loss: 1.9614
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1026
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0782  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2437 - loss: 2.0677
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[1m155/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2416 - loss: 2.0666
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[1m235/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2428 - loss: 2.0639
[1m276/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2436 - loss: 2.0617
[1m317/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.0596
[1m355/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2449 - loss: 2.0578
[1m395/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.0559
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Epoch 10/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.0248  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.0187
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[1m155/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2510 - loss: 2.0140
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[1m308/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2580 - loss: 2.0094
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[1m383/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0091
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7923
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2764 - loss: 1.9168  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 1.9445
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[1m526/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2677 - loss: 1.9760
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2683 - loss: 1.9760 - val_accuracy: 0.3232 - val_loss: 1.8607
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0920
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0068  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 1.9899
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Epoch 13/125

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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8692
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2800 - loss: 1.9707  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2841 - loss: 1.9543
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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.4806
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3099 - loss: 1.8956  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3041 - loss: 1.9136
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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 2.0991
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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9385
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3046 - loss: 1.8879  
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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.9039
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3050 - loss: 1.8812  
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Epoch 19/125

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[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3037 - loss: 1.8949  
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0115
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.8504  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3216 - loss: 1.8502
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.5778
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7450  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3226 - loss: 1.8086 - val_accuracy: 0.3711 - val_loss: 1.7303
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.6906
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3160 - loss: 1.7880  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3233 - loss: 1.7897
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3334 - loss: 1.7899 - val_accuracy: 0.3689 - val_loss: 1.7351
Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5000 - loss: 1.5102
[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3585 - loss: 1.8046  
[1m 70/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3385 - loss: 1.8359
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[1m287/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.8210
[1m327/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3328 - loss: 1.8188
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[1m515/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.8104
[1m555/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3355 - loss: 1.8091
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3358 - loss: 1.8081 - val_accuracy: 0.3822 - val_loss: 1.7190
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4688 - loss: 1.7505
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3760 - loss: 1.7445  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3696 - loss: 1.7593
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Epoch 25/125

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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.4688 - loss: 1.4541
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.7762  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3481 - loss: 1.7786
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[1m192/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7744
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0224
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3385 - loss: 1.8516  
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Epoch 28/125

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Epoch 29/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3686 - loss: 1.7225  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3714 - loss: 1.7223
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Epoch 30/125

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[1m275/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3658 - loss: 1.7129
[1m318/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3650 - loss: 1.7141
[1m359/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3645 - loss: 1.7152
[1m397/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3641 - loss: 1.7160
[1m433/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3637 - loss: 1.7166
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[1m513/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3633 - loss: 1.7177
[1m554/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3631 - loss: 1.7184
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3750 - loss: 1.8834
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3787 - loss: 1.7127  
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.5785
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3602 - loss: 1.7024  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3607 - loss: 1.6965
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[1m236/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3623 - loss: 1.6912
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[1m356/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3638 - loss: 1.6935
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 550ms/step
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 745us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 5: 36.68 [%]
F1-score capturado en la ejecución 5: 35.77 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 68/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 750us/step
[1m136/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 746us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.85 [%]
Global F1 score (validation) = 36.22 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.11254252e-01 9.88703072e-02 1.47554517e-01 ... 1.25707879e-06
  4.91947860e-01 1.49223059e-02]
 [1.58855215e-01 1.99812636e-01 2.18703747e-01 ... 1.15960647e-05
  2.11437792e-01 1.03221815e-02]
 [1.95880622e-01 2.42493451e-01 2.39099875e-01 ... 3.45592634e-05
  7.19834417e-02 7.44844042e-03]
 ...
 [1.21788435e-01 1.86624035e-01 1.64460108e-01 ... 2.06742061e-06
  3.79232198e-01 7.44984718e-03]
 [1.19528644e-01 1.30318031e-01 1.29945755e-01 ... 3.36931203e-03
  3.57845545e-01 4.63430658e-02]
 [1.12586133e-01 1.48607612e-01 1.31999418e-01 ... 1.57226634e-03
  3.35281849e-01 4.11455743e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 46.2 [%]
Global accuracy score (test) = 34.97 [%]
Global F1 score (train) = 45.11 [%]
Global F1 score (test) = 33.99 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.03      0.05       184
 CAMINAR CON MÓVIL O LIBRO       0.23      0.22      0.22       184
       CAMINAR USUAL SPEED       0.23      0.10      0.14       184
            CAMINAR ZIGZAG       0.23      0.39      0.29       184
          DE PIE BARRIENDO       0.26      0.23      0.24       184
   DE PIE DOBLANDO TOALLAS       0.40      0.33      0.36       184
    DE PIE MOVIENDO LIBROS       0.35      0.28      0.31       184
          DE PIE USANDO PC       0.42      0.58      0.49       184
        FASE REPOSO CON K5       0.32      0.76      0.45       184
INCREMENTAL CICLOERGOMETRO       0.77      0.37      0.50       184
           SENTADO LEYENDO       0.32      0.36      0.34       184
         SENTADO USANDO PC       0.22      0.24      0.23       184
      SENTADO VIENDO LA TV       0.34      0.30      0.32       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.49      0.43       184
                    TROTAR       0.91      0.60      0.72       161

                  accuracy                           0.35      2737
                 macro avg       0.38      0.35      0.34      2737
              weighted avg       0.37      0.35      0.34      2737

2025-11-05 20:34:30.962651: 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`.
2025-11-05 20:34:30.974063: 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 00:00:1762371270.987195  120408 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371270.991260  120408 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371271.001082  120408 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371271.001100  120408 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371271.001102  120408 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371271.001104  120408 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:34:31.004327: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371273.277794  120408 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371274.927573  120539 service.cc:152] XLA service 0x71366000cc10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371274.927599  120539 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:34:34.960926: 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 00:00:1762371275.132019  120539 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371277.591138  120539 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:27[0m 4s/step - accuracy: 0.0625 - loss: 4.6242
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0692 - loss: 4.4333  
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[1m230/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0834 - loss: 4.0459
[1m265/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0845 - loss: 3.9951
[1m306/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0857 - loss: 3.9380
[1m348/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0868 - loss: 3.8831
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Epoch 2/125

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Epoch 3/125

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[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1309 - loss: 2.5674  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1349 - loss: 2.5589
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0312 - loss: 2.6587
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1448 - loss: 2.4787  
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Epoch 5/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2010 - loss: 2.3049  
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2188 - loss: 2.1598
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2135 - loss: 2.2200  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.2175
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[1m509/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2148 - loss: 2.2040
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0809
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2552 - loss: 2.0931  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2490 - loss: 2.1193
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Epoch 8/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2441 - loss: 2.0896 - val_accuracy: 0.2781 - val_loss: 1.9674
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1389
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2461 - loss: 2.0201  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2426 - loss: 2.0353
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[1m273/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2447 - loss: 2.0460
[1m314/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.0461
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 2.0378
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2734 - loss: 1.9920  
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[1m191/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2692 - loss: 2.0157
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1381
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2479 - loss: 2.0414  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.0249
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[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2701 - loss: 1.9862
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2701 - loss: 1.9862 - val_accuracy: 0.3130 - val_loss: 1.8631
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2188 - loss: 1.9093
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2390 - loss: 2.0348  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.0102
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[1m233/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2648 - loss: 1.9817
[1m275/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2660 - loss: 1.9801
[1m316/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 1.9784
[1m354/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 1.9767
[1m393/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2689 - loss: 1.9750
[1m434/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2701 - loss: 1.9730
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[1m515/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2717 - loss: 1.9700
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2726 - loss: 1.9682 - val_accuracy: 0.3214 - val_loss: 1.8298
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4375 - loss: 1.9785
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3020 - loss: 1.9710  
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Epoch 14/125

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Epoch 15/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 1.9607  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2792 - loss: 1.9378
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Epoch 16/125

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Epoch 17/125

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Epoch 18/125

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Epoch 19/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.7570  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.7771
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 29ms/step - accuracy: 0.3438 - loss: 1.6333
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3277 - loss: 1.8050  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3292 - loss: 1.8179 - val_accuracy: 0.3739 - val_loss: 1.7096
Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8766
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3179 - loss: 1.8318  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3182 - loss: 1.8422
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3438 - loss: 1.7435
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9728
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.9894
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7627  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7560
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Epoch 25/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7647  
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Epoch 26/125

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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4688 - loss: 1.7559
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3792 - loss: 1.6887  
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.6032
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.9611
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.7201  
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8371
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7446  
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.6435
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6851
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3862 - loss: 1.6596  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3884 - loss: 1.6752
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Epoch 34/125

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[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 760us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 6: 34.97 [%]
F1-score capturado en la ejecución 6: 33.99 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 69/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 737us/step
[1m140/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 725us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 36.97 [%]
Global F1 score (validation) = 35.08 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[9.74107906e-02 9.18633193e-02 1.33285537e-01 ... 2.83430563e-06
  5.67135394e-01 1.15524046e-02]
 [2.26450548e-01 2.10121438e-01 2.90375859e-01 ... 1.06081984e-07
  8.16359371e-02 1.85845944e-03]
 [1.31228924e-01 2.30460465e-01 1.62177622e-01 ... 2.44558137e-03
  5.11975214e-02 8.51071533e-03]
 ...
 [1.59602702e-01 1.66189536e-01 1.76986843e-01 ... 8.67501967e-06
  3.53868395e-01 1.70136467e-02]
 [1.22572906e-01 1.19529389e-01 1.29227012e-01 ... 4.99647984e-04
  4.19264793e-01 5.02474196e-02]
 [1.20120980e-01 1.50517389e-01 1.39245242e-01 ... 8.97110614e-04
  3.38789552e-01 3.04698329e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.07 [%]
Global accuracy score (test) = 35.7 [%]
Global F1 score (train) = 45.27 [%]
Global F1 score (test) = 34.3 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.17      0.22       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.46      0.35       184
       CAMINAR USUAL SPEED       0.26      0.26      0.26       184
            CAMINAR ZIGZAG       0.00      0.00      0.00       184
          DE PIE BARRIENDO       0.23      0.16      0.19       184
   DE PIE DOBLANDO TOALLAS       0.40      0.45      0.42       184
    DE PIE MOVIENDO LIBROS       0.29      0.12      0.18       184
          DE PIE USANDO PC       0.41      0.61      0.49       184
        FASE REPOSO CON K5       0.29      0.76      0.41       184
INCREMENTAL CICLOERGOMETRO       0.82      0.33      0.47       184
           SENTADO LEYENDO       0.25      0.30      0.28       184
         SENTADO USANDO PC       0.23      0.33      0.27       184
      SENTADO VIENDO LA TV       0.54      0.28      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.54      0.49       184
                    TROTAR       0.95      0.61      0.75       161

                  accuracy                           0.36      2737
                 macro avg       0.38      0.36      0.34      2737
              weighted avg       0.38      0.36      0.34      2737

2025-11-05 20:35:29.001339: 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`.
2025-11-05 20:35:29.012853: 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 00:00:1762371329.026261  124874 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371329.030300  124874 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371329.040333  124874 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371329.040350  124874 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371329.040352  124874 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371329.040353  124874 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:35:29.043323: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371331.288534  124874 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371332.952539  124981 service.cc:152] XLA service 0x77db30002ea0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371332.952587  124981 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:35:32.995473: 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 00:00:1762371333.165043  124981 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371335.647616  124981 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 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3853
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.3078  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2051 - loss: 2.3185
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1992 - loss: 2.3238
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[1m352/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1956 - loss: 2.3178
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1190
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2215 - loss: 2.2003  
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.1621
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2498 - loss: 2.1495  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2510 - loss: 2.1434
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2380 - loss: 2.1375 - val_accuracy: 0.2810 - val_loss: 1.9715
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0362
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.1503  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.1507
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[1m261/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.1224
[1m299/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.1195
[1m339/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2436 - loss: 2.1166
[1m375/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.1144
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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.9393
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2583 - loss: 2.0089  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2505 - loss: 2.0187
[1m120/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2491 - loss: 2.0235
[1m157/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2493 - loss: 2.0270
[1m197/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2496 - loss: 2.0294
[1m232/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0307
[1m272/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2508 - loss: 2.0313
[1m311/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.0316
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[1m381/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2518 - loss: 2.0323
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2521 - loss: 2.0337 - val_accuracy: 0.2984 - val_loss: 1.9300
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 1.9080
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2599 - loss: 2.0522  
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[1m529/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0112
[1m567/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2617 - loss: 2.0109
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2618 - loss: 2.0109 - val_accuracy: 0.3031 - val_loss: 1.8810
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 1.7592
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2805 - loss: 1.9894  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2768 - loss: 1.9968
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[1m238/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2754 - loss: 1.9881
[1m277/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.9865
[1m314/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2754 - loss: 1.9859
[1m354/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2751 - loss: 1.9858
[1m388/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2748 - loss: 1.9857
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2744 - loss: 1.9841 - val_accuracy: 0.3204 - val_loss: 1.8467
Epoch 12/125

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[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2536 - loss: 1.9787  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 1.9795
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Epoch 13/125

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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.0435
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2864 - loss: 1.9791  
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Epoch 15/125

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Epoch 16/125

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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.9263
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3115 - loss: 1.8550  
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Epoch 18/125

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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4688 - loss: 1.9364
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.8353  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3332 - loss: 1.8348
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[1m575/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3255 - loss: 1.8398
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7637
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3602 - loss: 1.7869  
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Epoch 21/125

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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6740
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.7457  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7693
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8765
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3327 - loss: 1.7797  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7642
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4688 - loss: 1.5852
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[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.7558
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[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.7705
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3482 - loss: 1.7705 - val_accuracy: 0.3819 - val_loss: 1.7127
Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7167
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3688 - loss: 1.6911  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3626 - loss: 1.7140
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[1m235/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7490
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3531 - loss: 1.7537
[1m347/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3527 - loss: 1.7557
[1m385/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.7574
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[1m535/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7611
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1835
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7928  
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Epoch 27/125

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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4688 - loss: 1.6022
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.7499  
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Epoch 29/125

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Epoch 30/125

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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.4553
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3687 - loss: 1.6964  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3769 - loss: 1.6963
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Epoch 32/125

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[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3724 - loss: 1.6890  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.7082
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.7494
[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3890 - loss: 1.7126  
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4375 - loss: 1.4927
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4090 - loss: 1.5948  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3992 - loss: 1.6239
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Epoch 35/125

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[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3888 - loss: 1.6570  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 1.7471
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.6672  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3914 - loss: 1.6768 - val_accuracy: 0.3837 - val_loss: 1.6526
Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.8440
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3981 - loss: 1.6175  
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.9294
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3835 - loss: 1.7530  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3899 - loss: 1.7203
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[1m317/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3958 - loss: 1.6770
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[1m508/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3970 - loss: 1.6663
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Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.5404
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3982 - loss: 1.6077  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3928 - loss: 1.6188
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[1m515/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3879 - loss: 1.6441
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3876 - loss: 1.6456 - val_accuracy: 0.3713 - val_loss: 1.6805
Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4688 - loss: 1.4799
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4110 - loss: 1.6385  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4042 - loss: 1.6442
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Epoch 41/125

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[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 791us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 7: 35.7 [%]
F1-score capturado en la ejecución 7: 34.3 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 730us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 66/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 772us/step
[1m132/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 771us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.65 [%]
Global F1 score (validation) = 36.76 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.1523132e-01 1.4417432e-01 1.5355563e-01 ... 7.8251423e-09
  4.6619955e-01 4.0349076e-03]
 [1.2863819e-01 2.2598903e-01 2.0361525e-01 ... 2.9928383e-08
  2.9523447e-01 5.2824980e-03]
 [1.5741612e-01 3.4797850e-01 2.4851370e-01 ... 4.3734003e-06
  4.4782512e-02 3.1259730e-03]
 ...
 [1.0848366e-01 2.3531902e-01 1.7968373e-01 ... 1.1285546e-06
  3.6429894e-01 3.9940607e-03]
 [1.0935572e-01 1.3411821e-01 1.3705654e-01 ... 2.1884260e-04
  4.5085549e-01 2.6311757e-02]
 [6.7135729e-02 1.6383535e-01 1.1272654e-01 ... 4.6637324e-06
  5.5322468e-01 4.5665917e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.82 [%]
Global accuracy score (test) = 34.6 [%]
Global F1 score (train) = 45.0 [%]
Global F1 score (test) = 32.47 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.63      0.34       184
       CAMINAR USUAL SPEED       0.33      0.04      0.07       184
            CAMINAR ZIGZAG       0.19      0.09      0.12       184
          DE PIE BARRIENDO       0.23      0.20      0.21       184
   DE PIE DOBLANDO TOALLAS       0.29      0.43      0.35       184
    DE PIE MOVIENDO LIBROS       0.31      0.21      0.25       184
          DE PIE USANDO PC       0.37      0.48      0.42       184
        FASE REPOSO CON K5       0.29      0.62      0.40       184
INCREMENTAL CICLOERGOMETRO       0.80      0.46      0.59       184
           SENTADO LEYENDO       0.30      0.49      0.37       184
         SENTADO USANDO PC       0.27      0.16      0.20       184
      SENTADO VIENDO LA TV       0.41      0.25      0.31       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.52      0.48       184
                    TROTAR       0.91      0.66      0.76       161

                  accuracy                           0.35      2737
                 macro avg       0.36      0.35      0.32      2737
              weighted avg       0.36      0.35      0.32      2737

2025-11-05 20:36:34.417291: 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`.
2025-11-05 20:36:34.428526: 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 00:00:1762371394.441608  130028 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371394.445688  130028 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371394.455437  130028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371394.455455  130028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371394.455456  130028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371394.455457  130028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:36:34.458643: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371396.751112  130028 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371398.410543  130138 service.cc:152] XLA service 0x77a15c003ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371398.410578  130138 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:36:38.450421: 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 00:00:1762371398.620423  130138 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371401.078598  130138 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 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2009 - loss: 2.3471  
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Epoch 6/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2061 - loss: 2.2302  
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Epoch 7/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2225 - loss: 2.1648 - val_accuracy: 0.2379 - val_loss: 2.0450
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.2744
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2148 - loss: 2.1610  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.1563
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Epoch 9/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2825 - loss: 2.0258  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 2.0430
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[1m316/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2485 - loss: 2.0644
[1m359/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.0655
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2480 - loss: 2.0660 - val_accuracy: 0.2836 - val_loss: 1.9476
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0625 - loss: 2.4862
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2357 - loss: 2.0319  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0179
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[1m515/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2504 - loss: 2.0287
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2511 - loss: 2.0287 - val_accuracy: 0.2964 - val_loss: 1.9020
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9130
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2676 - loss: 2.0112  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2618 - loss: 2.0236
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[1m148/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0170
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Epoch 12/125

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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.0294
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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8790
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2755 - loss: 1.9357  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2864 - loss: 1.9148
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Epoch 15/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2610 - loss: 1.9653  
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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.0574
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2913 - loss: 1.9091 - val_accuracy: 0.3236 - val_loss: 1.8042
Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.5904
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3329 - loss: 1.8338  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.8479
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Epoch 18/125

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[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2916 - loss: 1.8588  
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 1.8730
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3100 - loss: 1.8589 - val_accuracy: 0.3256 - val_loss: 1.7932
Epoch 20/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3190 - loss: 1.8996  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3170 - loss: 1.8919
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Epoch 21/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3150 - loss: 1.8607  
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Epoch 22/125

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Epoch 23/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3268 - loss: 1.8264  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.8339
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Epoch 24/125

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Epoch 25/125

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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4688 - loss: 2.0755
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3738 - loss: 1.7595  
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8042
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.5850
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 2.0226
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3606 - loss: 1.8064  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.7771
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Epoch 30/125

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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.5624
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3918 - loss: 1.6563  
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.8431
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7571  
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Epoch 33/125

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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.7350
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3708 - loss: 1.7884  
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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9621
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4045 - loss: 1.6447  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8909
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3852 - loss: 1.6797  
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4099
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.6569  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3769 - loss: 1.6542
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[1m274/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3765 - loss: 1.6654
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.6763
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.6800  
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[1m192/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3701 - loss: 1.6760
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Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.9173
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3799 - loss: 1.6514  
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Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.3607
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3808 - loss: 1.6189  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.6341
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Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.4400
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4310 - loss: 1.5366  
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[1m318/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3985 - loss: 1.6472
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[1m389/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3971 - loss: 1.6507
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[1m569/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3951 - loss: 1.6542
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3949 - loss: 1.6544 - val_accuracy: 0.3665 - val_loss: 1.6717
Epoch 42/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4062 - loss: 1.5204
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3932 - loss: 1.6168  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3918 - loss: 1.6448 - val_accuracy: 0.3819 - val_loss: 1.6594
Epoch 43/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6799
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3821 - loss: 1.7085  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3815 - loss: 1.7032
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[1m357/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3907 - loss: 1.6578
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[1m583/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3918 - loss: 1.6528
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 576ms/step
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 800us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 8: 34.6 [%]
F1-score capturado en la ejecución 8: 32.47 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 802us/step
[1m135/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 756us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.2 [%]
Global F1 score (validation) = 37.76 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[8.24783891e-02 9.15985778e-02 1.40753970e-01 ... 9.59735985e-07
  6.05626523e-01 1.27071822e-02]
 [1.87481523e-01 2.37371728e-01 2.95472652e-01 ... 7.26193221e-08
  1.42132744e-01 2.69869366e-03]
 [2.06947789e-01 2.79733032e-01 2.61953294e-01 ... 2.17834145e-06
  7.95617923e-02 4.73467913e-03]
 ...
 [1.79215401e-01 1.66140229e-01 2.18634561e-01 ... 9.98392079e-06
  2.61624277e-01 2.62868740e-02]
 [1.30720958e-01 1.61814079e-01 1.80909842e-01 ... 1.26740066e-04
  3.91466022e-01 2.26888228e-02]
 [1.09170444e-01 2.04221159e-01 1.85886264e-01 ... 4.22107078e-05
  3.92632604e-01 1.39145050e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 48.25 [%]
Global accuracy score (test) = 35.73 [%]
Global F1 score (train) = 46.64 [%]
Global F1 score (test) = 34.92 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.23      0.26       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.34      0.28       184
       CAMINAR USUAL SPEED       0.27      0.27      0.27       184
            CAMINAR ZIGZAG       0.00      0.00      0.00       184
          DE PIE BARRIENDO       0.21      0.20      0.20       184
   DE PIE DOBLANDO TOALLAS       0.31      0.38      0.34       184
    DE PIE MOVIENDO LIBROS       0.42      0.26      0.32       184
          DE PIE USANDO PC       0.42      0.60      0.50       184
        FASE REPOSO CON K5       0.34      0.75      0.46       184
INCREMENTAL CICLOERGOMETRO       0.90      0.45      0.60       184
           SENTADO LEYENDO       0.28      0.24      0.26       184
         SENTADO USANDO PC       0.19      0.22      0.21       184
      SENTADO VIENDO LA TV       0.40      0.30      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.38      0.55      0.45       184
                    TROTAR       0.94      0.62      0.75       161

                  accuracy                           0.36      2737
                 macro avg       0.37      0.36      0.35      2737
              weighted avg       0.37      0.36      0.35      2737

2025-11-05 20:37:41.624862: 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`.
2025-11-05 20:37:41.636029: 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 00:00:1762371461.649102  135357 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371461.653165  135357 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371461.662800  135357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371461.662816  135357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371461.662818  135357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371461.662819  135357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:37:41.665921: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371463.916776  135357 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371465.551504  135494 service.cc:152] XLA service 0x7169ac00bb50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371465.551529  135494 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:37:45.583927: 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 00:00:1762371465.748774  135494 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371468.184710  135494 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:08[0m 4s/step - accuracy: 0.2188 - loss: 4.3914
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[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0825 - loss: 4.2876
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[1m194/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0853 - loss: 4.0680
[1m232/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0867 - loss: 4.0078
[1m274/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0883 - loss: 3.9454
[1m311/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0896 - loss: 3.8937
[1m352/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0909 - loss: 3.8398
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Epoch 2/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.1250 - loss: 2.6141
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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5843
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1578 - loss: 2.5045  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1560 - loss: 2.5017
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[1m348/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1566 - loss: 2.4965
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.5260
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1656 - loss: 2.4803  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1649 - loss: 2.4666
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Epoch 5/125

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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1452
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2498 - loss: 2.1303  
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 1.9093
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2337 - loss: 2.0945  
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5000 - loss: 1.8169
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2472 - loss: 2.0672 - val_accuracy: 0.2562 - val_loss: 1.9967
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.2410
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0851  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2485 - loss: 2.0647
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[1m341/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2510 - loss: 2.0412
[1m382/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2511 - loss: 2.0409
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 1.9383
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2602 - loss: 2.0311  
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1628
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2737 - loss: 1.9828 - val_accuracy: 0.3267 - val_loss: 1.8802
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0311
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2691 - loss: 2.0053  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2706 - loss: 1.9846
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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.9117
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2911 - loss: 1.9211  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2900 - loss: 1.9150
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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.6977
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3189 - loss: 1.8621  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.8772
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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.8445
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3088 - loss: 1.8494  
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Epoch 17/125

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Epoch 18/125

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[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2970 - loss: 1.8852  
[1m 69/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3063 - loss: 1.8821
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 1.7814
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Epoch 20/125

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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 1.7682
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.8009  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3099 - loss: 1.8044
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Epoch 22/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.8061  
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7229
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.5000 - loss: 1.4884
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.7590  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3580 - loss: 1.7451
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[1m308/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.7607
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3750 - loss: 1.8029
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.7947
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.7321  
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7180
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3668 - loss: 1.7378  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.7529
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.9623
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 1.7266
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.7240  
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.6145
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.7326  
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.9038
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3814 - loss: 1.7565  
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8608
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.6883  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3680 - loss: 1.6866
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.5735
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.6567  
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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8913
[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3601 - loss: 1.7321  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 2.1550
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3944 - loss: 1.6565  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4005 - loss: 1.6384
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.5808
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4037 - loss: 1.6320  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3988 - loss: 1.6460
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8128
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3895 - loss: 1.6810  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3887 - loss: 1.6670
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3871 - loss: 1.6601
[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3869 - loss: 1.6605
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[1m573/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3856 - loss: 1.6627
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3856 - loss: 1.6629 - val_accuracy: 0.3752 - val_loss: 1.6617
Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4375 - loss: 1.9917
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3975 - loss: 1.6995  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3919 - loss: 1.6932
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3858 - loss: 1.6843
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3886 - loss: 1.6722 - val_accuracy: 0.3926 - val_loss: 1.7092
Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.6770
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4089 - loss: 1.6654  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4045 - loss: 1.6584
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Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9954
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4050 - loss: 1.6257  
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[1m238/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3938 - loss: 1.6460
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[1m317/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3919 - loss: 1.6494
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[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 768us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 9: 35.73 [%]
F1-score capturado en la ejecución 9: 34.92 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m347/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 729us/step
[1m417/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 728us/step
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 743us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 61/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 841us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 758us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.87 [%]
Global F1 score (validation) = 38.18 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[6.7159757e-02 5.0776783e-02 9.9788614e-02 ... 3.2928668e-08
  7.1661669e-01 4.7385800e-03]
 [1.5757626e-01 1.9955967e-01 2.4064651e-01 ... 2.3149477e-07
  2.3944512e-01 1.8449590e-03]
 [1.7163754e-01 3.0545393e-01 2.5163987e-01 ... 5.0637723e-06
  9.0761773e-02 1.6162818e-03]
 ...
 [1.4457588e-01 1.8434161e-01 2.0211551e-01 ... 3.7373109e-06
  3.2203096e-01 9.5850974e-03]
 [1.3368399e-01 1.5978587e-01 1.6441229e-01 ... 6.6282094e-04
  3.5946217e-01 2.4687303e-02]
 [8.5642233e-02 1.6088445e-01 1.3367775e-01 ... 2.5721645e-05
  4.9426907e-01 7.4980394e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 49.26 [%]
Global accuracy score (test) = 34.2 [%]
Global F1 score (train) = 47.1 [%]
Global F1 score (test) = 32.5 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.36      0.30       184
       CAMINAR USUAL SPEED       0.16      0.14      0.15       184
            CAMINAR ZIGZAG       0.19      0.17      0.18       184
          DE PIE BARRIENDO       0.40      0.49      0.44       184
   DE PIE DOBLANDO TOALLAS       0.31      0.26      0.28       184
    DE PIE MOVIENDO LIBROS       0.46      0.12      0.20       184
          DE PIE USANDO PC       0.51      0.53      0.52       184
        FASE REPOSO CON K5       0.28      0.79      0.42       184
INCREMENTAL CICLOERGOMETRO       0.67      0.54      0.60       184
           SENTADO LEYENDO       0.23      0.27      0.24       184
         SENTADO USANDO PC       0.13      0.10      0.12       184
      SENTADO VIENDO LA TV       0.35      0.25      0.29       184
   SUBIR Y BAJAR ESCALERAS       0.37      0.56      0.44       184
                    TROTAR       0.87      0.57      0.69       161

                  accuracy                           0.34      2737
                 macro avg       0.35      0.34      0.32      2737
              weighted avg       0.34      0.34      0.32      2737

2025-11-05 20:38:46.682591: 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`.
2025-11-05 20:38:46.694226: 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 00:00:1762371526.707466  140503 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371526.711689  140503 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371526.721577  140503 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371526.721595  140503 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371526.721597  140503 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371526.721598  140503 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:38:46.724797: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371529.031921  140503 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371530.693329  140614 service.cc:152] XLA service 0x7878c8004e60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371530.693361  140614 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:38:50.734574: 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 00:00:1762371530.898944  140614 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371533.334023  140614 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:19[0m 4s/step - accuracy: 0.0938 - loss: 4.3720
[1m 31/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0880 - loss: 4.2752  
[1m 68/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0836 - loss: 4.2340
[1m106/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0831 - loss: 4.1749
[1m142/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0838 - loss: 4.1175
[1m185/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0850 - loss: 4.0486
[1m225/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0858 - loss: 3.9872
[1m264/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0862 - loss: 3.9317
[1m305/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0866 - loss: 3.8764
[1m344/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0872 - loss: 3.8270
[1m386/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0878 - loss: 3.7774
[1m428/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0885 - loss: 3.7318
[1m466/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0892 - loss: 3.6937
[1m507/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0898 - loss: 3.6555
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

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[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1551 - loss: 2.4683  
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Epoch 5/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1830 - loss: 2.2677  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1869 - loss: 2.2789
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[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1927 - loss: 2.2866
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[1m381/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.2853
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2115
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2064 - loss: 2.2001  
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[1m537/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2164 - loss: 2.1953
[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2167 - loss: 2.1943
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2167 - loss: 2.1941 - val_accuracy: 0.2477 - val_loss: 2.0586
Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.1842
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2392 - loss: 2.0817  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2362 - loss: 2.1051
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2284 - loss: 2.1231 - val_accuracy: 0.2586 - val_loss: 2.0094
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1549
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2366 - loss: 2.1001  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2417 - loss: 2.0899
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2442 - loss: 2.0869
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2447 - loss: 2.0816 - val_accuracy: 0.2842 - val_loss: 1.9594
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0081
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2364 - loss: 2.0813  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2472 - loss: 2.0585
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[1m544/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2580 - loss: 2.0480
[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2581 - loss: 2.0478
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2581 - loss: 2.0477 - val_accuracy: 0.3079 - val_loss: 1.9207
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.8679
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2595 - loss: 1.9900  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0013
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[1m506/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2596 - loss: 2.0144
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Epoch 11/125

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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 1.9021
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2648 - loss: 1.9608  
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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 2.2819
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2813 - loss: 1.9905  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2785 - loss: 1.9740
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[1m569/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2821 - loss: 1.9414
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2822 - loss: 1.9412 - val_accuracy: 0.3138 - val_loss: 1.8311
Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 24ms/step - accuracy: 0.3438 - loss: 1.8190
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2882 - loss: 1.9389  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2892 - loss: 1.9335
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2917 - loss: 1.9220 - val_accuracy: 0.3389 - val_loss: 1.8310
Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8868
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3088 - loss: 1.8778  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3066 - loss: 1.8884
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3004 - loss: 1.9004
[1m347/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3002 - loss: 1.9015
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[1m537/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3000 - loss: 1.9006
[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2998 - loss: 1.9005
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2998 - loss: 1.9004 - val_accuracy: 0.3441 - val_loss: 1.7823
Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.7489
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3134 - loss: 1.9015  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3037 - loss: 1.8881
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Epoch 17/125

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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.9773
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 1.8823
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2968 - loss: 1.8210  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3050 - loss: 1.8407
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Epoch 20/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3543 - loss: 1.7700  
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 2.0329
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3310 - loss: 1.8431  
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.8260
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3281 - loss: 1.8743  
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.5597
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.7932  
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.9795
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3275 - loss: 1.8002  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3363 - loss: 1.7820
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6259
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.7190  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7387
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Epoch 26/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.7722  
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9059
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3633 - loss: 1.7537  
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4688 - loss: 1.4895
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.6968
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.7766
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3854 - loss: 1.7089  
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[1m384/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3717 - loss: 1.7120
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.6078
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3659 - loss: 1.7176  
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3726 - loss: 1.7104
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Epoch 32/125

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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8700
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3880 - loss: 1.6888  
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.5000 - loss: 1.3311
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Epoch 35/125

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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6707
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3824 - loss: 1.6591  
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.7761
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.6972  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3888 - loss: 1.6905
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.5632
[1m 31/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4193 - loss: 1.5930  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4114 - loss: 1.6171
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3892 - loss: 1.6532 - val_accuracy: 0.3939 - val_loss: 1.6764
Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5000 - loss: 1.6721
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4264 - loss: 1.6316  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4160 - loss: 1.6313
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Epoch 40/125

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Saved model to disk.

Accuracy capturado en la ejecución 10: 34.2 [%]
F1-score capturado en la ejecución 10: 32.5 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 790us/step
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[1m 70/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 729us/step
[1m135/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 752us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.2 [%]
Global F1 score (validation) = 36.37 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[7.75037333e-02 4.84340973e-02 7.02511817e-02 ... 1.61279354e-06
  7.04779685e-01 2.43433136e-02]
 [2.09741399e-01 1.99313506e-01 1.96730018e-01 ... 3.77366200e-06
  2.07832158e-01 1.27442963e-02]
 [1.97004989e-01 2.92887598e-01 2.06434786e-01 ... 7.54640787e-05
  8.60070437e-02 1.09364195e-02]
 ...
 [1.69474468e-01 2.18373522e-01 1.98028326e-01 ... 4.50878588e-06
  2.84236431e-01 5.07179741e-03]
 [1.27721012e-01 1.45164669e-01 1.30048379e-01 ... 2.06237659e-03
  3.10947835e-01 3.07132006e-02]
 [1.27224445e-01 2.17097610e-01 1.61825091e-01 ... 1.71909327e-04
  3.08357894e-01 1.02191940e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.68 [%]
Global accuracy score (test) = 36.5 [%]
Global F1 score (train) = 44.75 [%]
Global F1 score (test) = 34.55 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.33      0.29       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.37      0.28       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.00      0.00      0.00       184
          DE PIE BARRIENDO       0.23      0.35      0.28       184
   DE PIE DOBLANDO TOALLAS       0.49      0.34      0.40       184
    DE PIE MOVIENDO LIBROS       0.31      0.18      0.23       184
          DE PIE USANDO PC       0.31      0.60      0.41       184
        FASE REPOSO CON K5       0.36      0.83      0.50       184
INCREMENTAL CICLOERGOMETRO       0.84      0.38      0.52       184
           SENTADO LEYENDO       0.39      0.36      0.38       184
         SENTADO USANDO PC       0.27      0.34      0.30       184
      SENTADO VIENDO LA TV       0.70      0.25      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.44      0.57      0.50       184
                    TROTAR       0.93      0.62      0.75       161

                  accuracy                           0.36      2737
                 macro avg       0.38      0.37      0.35      2737
              weighted avg       0.38      0.36      0.34      2737

2025-11-05 20:39:50.838338: 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`.
2025-11-05 20:39:50.849526: 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 00:00:1762371590.862394  145549 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371590.866428  145549 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371590.876090  145549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371590.876107  145549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371590.876109  145549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371590.876110  145549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:39:50.879201: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371593.145396  145549 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371594.792292  145680 service.cc:152] XLA service 0x713e7c00cda0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371594.792327  145680 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:39:54.828163: 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 00:00:1762371594.998898  145680 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371597.421043  145680 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 2/125

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Epoch 3/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1572 - loss: 2.5103 - val_accuracy: 0.1856 - val_loss: 2.3815
Epoch 4/125

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[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1453 - loss: 2.4661  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1567 - loss: 2.4525
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[1m568/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1715 - loss: 2.4203
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1717 - loss: 2.4195 - val_accuracy: 0.2057 - val_loss: 2.2098
Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.1422
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1886 - loss: 2.3089  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1911 - loss: 2.3065
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1952 - loss: 2.2995
[1m239/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1963 - loss: 2.2979
[1m281/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1972 - loss: 2.2962
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[1m389/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1985 - loss: 2.2920
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Epoch 6/125

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[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2218 - loss: 2.2210  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2214 - loss: 2.2064
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 2.0384
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2211 - loss: 2.1687  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.1605
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[1m515/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2294 - loss: 2.1389
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2298 - loss: 2.1365 - val_accuracy: 0.2729 - val_loss: 1.9887
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3111
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2416 - loss: 2.1432  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2416 - loss: 2.1254
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[1m275/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.0982
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Epoch 9/125

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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8208
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2660 - loss: 2.0087 - val_accuracy: 0.3045 - val_loss: 1.9120
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1253
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2704 - loss: 2.0241  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2701 - loss: 2.0175
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[1m279/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2660 - loss: 1.9989
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Epoch 12/125

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Epoch 13/125

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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1267
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3000 - loss: 1.9413  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2952 - loss: 1.9342
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Epoch 15/125

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Epoch 16/125

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Epoch 17/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.8869  
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Epoch 18/125

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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.4608
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.9030
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.8151  
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Epoch 21/125

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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.8271
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.0880
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3335 - loss: 1.7988  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3382 - loss: 1.7925 - val_accuracy: 0.3526 - val_loss: 1.7431
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.8326
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3348 - loss: 1.7529  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7631
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3434 - loss: 1.7855 - val_accuracy: 0.3550 - val_loss: 1.7296
Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.4977
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3790 - loss: 1.6906  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.7123
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[1m341/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.7520
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[1m532/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.7571
[1m568/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.7579
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3521 - loss: 1.7584 - val_accuracy: 0.3636 - val_loss: 1.7026
Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8963
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3191 - loss: 1.8610  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3252 - loss: 1.8244
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Epoch 27/125

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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.8840
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.7713  
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9240
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3674 - loss: 1.7155  
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Epoch 30/125

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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7873
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Epoch 32/125

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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.4434
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.9325
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3567 - loss: 1.7852  
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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5000 - loss: 1.4236
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[1m389/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3671 - loss: 1.7023
[1m428/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3675 - loss: 1.7014
[1m467/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3680 - loss: 1.7005
[1m502/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3683 - loss: 1.7000
[1m544/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.6996
[1m583/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3687 - loss: 1.6993
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3687 - loss: 1.6993 - val_accuracy: 0.3835 - val_loss: 1.6941
Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.7434
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[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4031 - loss: 1.6395
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[1m232/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3921 - loss: 1.6583
[1m271/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3908 - loss: 1.6598
[1m312/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3897 - loss: 1.6607
[1m351/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3886 - loss: 1.6613
[1m391/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.6620
[1m428/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3870 - loss: 1.6627
[1m468/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3860 - loss: 1.6639
[1m509/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.6651
[1m551/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3844 - loss: 1.6662
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3838 - loss: 1.6670 - val_accuracy: 0.3839 - val_loss: 1.6704

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 564ms/step
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 796us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 11: 36.5 [%]
F1-score capturado en la ejecución 11: 34.55 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 69/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 744us/step
[1m135/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 753us/step
[1m204/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 746us/step
[1m272/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 745us/step
[1m336/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 754us/step
[1m409/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 743us/step
[1m469/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 755us/step
[1m543/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 745us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 837us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 70/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 731us/step
[1m137/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 743us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.39 [%]
Global F1 score (validation) = 37.64 [%]
[[ 2.]
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 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.3170107e-01 1.2409360e-01 1.3967645e-01 ... 3.8557082e-05
  4.4643906e-01 1.9581063e-02]
 [2.1114151e-01 2.4015993e-01 2.4428286e-01 ... 1.4784863e-06
  1.3655885e-01 2.8000795e-03]
 [4.9342129e-02 5.7422247e-02 4.5974884e-02 ... 5.3306771e-03
  4.6687491e-02 2.0892804e-02]
 ...
 [1.8721165e-01 1.6468178e-01 1.8775491e-01 ... 2.5408735e-07
  3.2153523e-01 5.5914288e-03]
 [1.2497700e-01 1.3341391e-01 1.3458671e-01 ... 6.9289439e-04
  4.3713289e-01 2.3305722e-02]
 [1.4459664e-01 1.7210029e-01 1.7451574e-01 ... 1.1071854e-05
  3.9007139e-01 6.6992422e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 46.94 [%]
Global accuracy score (test) = 36.57 [%]
Global F1 score (train) = 45.2 [%]
Global F1 score (test) = 35.23 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.28      0.28       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.43      0.32       184
       CAMINAR USUAL SPEED       0.30      0.13      0.18       184
            CAMINAR ZIGZAG       0.00      0.00      0.00       184
          DE PIE BARRIENDO       0.30      0.36      0.32       184
   DE PIE DOBLANDO TOALLAS       0.36      0.25      0.29       184
    DE PIE MOVIENDO LIBROS       0.40      0.37      0.38       184
          DE PIE USANDO PC       0.41      0.58      0.48       184
        FASE REPOSO CON K5       0.29      0.80      0.43       184
INCREMENTAL CICLOERGOMETRO       0.89      0.34      0.49       184
           SENTADO LEYENDO       0.29      0.24      0.26       184
         SENTADO USANDO PC       0.28      0.18      0.22       184
      SENTADO VIENDO LA TV       0.37      0.38      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.54      0.48       184
                    TROTAR       0.95      0.64      0.77       161

                  accuracy                           0.37      2737
                 macro avg       0.39      0.37      0.35      2737
              weighted avg       0.38      0.37      0.35      2737

2025-11-05 20:40:50.617064: 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`.
2025-11-05 20:40:50.628257: 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 00:00:1762371650.641396  150197 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371650.645625  150197 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371650.655447  150197 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371650.655475  150197 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371650.655476  150197 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371650.655477  150197 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:40:50.658608: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371652.911806  150197 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371654.541380  150328 service.cc:152] XLA service 0x7ca42c00be60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371654.541422  150328 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:40:54.577086: 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 00:00:1762371654.740744  150328 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371657.136750  150328 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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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0876 - loss: 3.9888
[1m350/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0887 - loss: 3.9323
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[1m503/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0920 - loss: 3.7553
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3125
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1448 - loss: 2.4553  
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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.4937
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Epoch 6/125

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[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2181 - loss: 2.2143  
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.2417
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2341 - loss: 2.1504  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2319 - loss: 2.1451
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[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2250 - loss: 2.1478
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2256 - loss: 2.1460 - val_accuracy: 0.2601 - val_loss: 2.0202
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3750 - loss: 1.9105
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0702  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0970
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2401 - loss: 2.1069 - val_accuracy: 0.2772 - val_loss: 1.9831
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 1.9606
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2525 - loss: 2.0315  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2559 - loss: 2.0550 - val_accuracy: 0.2923 - val_loss: 1.9244
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0904
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.0342  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2453 - loss: 2.0314
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[1m289/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2521 - loss: 2.0313
[1m330/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2530 - loss: 2.0304
[1m372/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2537 - loss: 2.0296
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2564 - loss: 2.0245 - val_accuracy: 0.2818 - val_loss: 1.8935
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.0238
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2375 - loss: 2.0336  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2515 - loss: 2.0138
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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.9186
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2826 - loss: 1.9637 - val_accuracy: 0.3101 - val_loss: 1.8726
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.4080
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3310 - loss: 1.8478  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.8802
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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.8764
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2748 - loss: 1.9401  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2760 - loss: 1.9430
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Epoch 15/125

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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0166
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3029 - loss: 1.9182  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2990 - loss: 1.9124
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2955 - loss: 1.8999
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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1301
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Epoch 18/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3100 - loss: 1.8533 - val_accuracy: 0.3486 - val_loss: 1.7461
Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8904
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2934 - loss: 1.8695  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2968 - loss: 1.8682
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[1m311/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3046 - loss: 1.8616
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8488
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.8242  
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Epoch 21/125

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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7162
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7683  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3329 - loss: 1.7911
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Epoch 23/125

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[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3208 - loss: 1.7996  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3274 - loss: 1.7941
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7015
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3416 - loss: 1.7708 - val_accuracy: 0.3750 - val_loss: 1.7157
Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 2.0784
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.8286  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7986
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Epoch 26/125

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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.8223
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3421 - loss: 1.7420  
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 1.9847
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.7623  
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Epoch 29/125

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[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.7095
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4982
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3731 - loss: 1.6759  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3689 - loss: 1.7142 - val_accuracy: 0.3772 - val_loss: 1.6870
Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.3129
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.6986  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.7070
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[1m503/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.7123
[1m541/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3618 - loss: 1.7124
[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.7124
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3622 - loss: 1.7124 - val_accuracy: 0.3774 - val_loss: 1.6762
Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9985
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3841 - loss: 1.7024  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3791 - loss: 1.6970
[1m114/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3757 - loss: 1.6957
[1m152/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3734 - loss: 1.6955
[1m190/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3717 - loss: 1.6942
[1m224/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3707 - loss: 1.6939
[1m260/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.6944
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[1m339/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3695 - loss: 1.6964
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[1m455/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3698 - loss: 1.6968
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[1m533/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3703 - loss: 1.6967
[1m572/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3706 - loss: 1.6966
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3707 - loss: 1.6966 - val_accuracy: 0.4031 - val_loss: 1.6600
Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8406
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3494 - loss: 1.7039  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3566 - loss: 1.6854
[1m113/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3602 - loss: 1.6840
[1m152/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.6850
[1m192/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3637 - loss: 1.6841
[1m230/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3645 - loss: 1.6843
[1m268/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3656 - loss: 1.6852
[1m308/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3666 - loss: 1.6863
[1m347/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3672 - loss: 1.6874
[1m389/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3679 - loss: 1.6880
[1m428/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.6881
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[1m507/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3695 - loss: 1.6881
[1m548/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3700 - loss: 1.6879
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3703 - loss: 1.6878 - val_accuracy: 0.4004 - val_loss: 1.6727

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 552ms/step
[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 784us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 12: 36.57 [%]
F1-score capturado en la ejecución 12: 35.23 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:41[0m 998ms/step
[1m 65/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 783us/step  
[1m137/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 738us/step
[1m208/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 728us/step
[1m279/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 723us/step
[1m352/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 716us/step
[1m416/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 727us/step
[1m487/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 725us/step
[1m559/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 721us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 762us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 66/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 779us/step
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 763us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.04 [%]
Global F1 score (validation) = 38.56 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[9.8059423e-02 9.5246606e-02 1.1282938e-01 ... 1.6598378e-06
  6.1001092e-01 2.4167120e-03]
 [1.6357408e-01 1.2220140e-01 1.5099734e-01 ... 3.0782878e-06
  4.1561571e-01 8.0996305e-03]
 [2.0597038e-01 2.5279143e-01 1.9687511e-01 ... 2.4762374e-04
  8.6009704e-02 1.6379857e-02]
 ...
 [1.7385444e-01 1.6283938e-01 1.7519961e-01 ... 3.3623280e-06
  3.4502223e-01 1.1408901e-02]
 [1.4417280e-01 1.4948931e-01 1.3750169e-01 ... 3.8134074e-03
  2.6723740e-01 2.8073149e-02]
 [1.5686783e-01 2.0672195e-01 1.6802725e-01 ... 1.2996902e-04
  2.8202772e-01 1.1774039e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.76 [%]
Global accuracy score (test) = 35.55 [%]
Global F1 score (train) = 45.55 [%]
Global F1 score (test) = 33.15 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.25      0.25       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.46      0.32       184
       CAMINAR USUAL SPEED       0.14      0.01      0.02       184
            CAMINAR ZIGZAG       0.15      0.05      0.08       184
          DE PIE BARRIENDO       0.29      0.24      0.27       184
   DE PIE DOBLANDO TOALLAS       0.38      0.32      0.34       184
    DE PIE MOVIENDO LIBROS       0.33      0.34      0.33       184
          DE PIE USANDO PC       0.42      0.58      0.49       184
        FASE REPOSO CON K5       0.31      0.75      0.44       184
INCREMENTAL CICLOERGOMETRO       0.81      0.45      0.58       184
           SENTADO LEYENDO       0.25      0.24      0.25       184
         SENTADO USANDO PC       0.00      0.00      0.00       184
      SENTADO VIENDO LA TV       0.29      0.54      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.50      0.46       184
                    TROTAR       0.95      0.65      0.77       161

                  accuracy                           0.36      2737
                 macro avg       0.35      0.36      0.33      2737
              weighted avg       0.35      0.36      0.33      2737

2025-11-05 20:41:47.637066: 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`.
2025-11-05 20:41:47.648611: 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 00:00:1762371707.661871  154558 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371707.665784  154558 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371707.675773  154558 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371707.675793  154558 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371707.675794  154558 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371707.675795  154558 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:41:47.678913: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371709.948588  154558 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371711.635220  154668 service.cc:152] XLA service 0x7b69d40043c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371711.635244  154668 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:41:51.668701: 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 00:00:1762371711.839285  154668 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371714.278401  154668 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:32[0m 4s/step - accuracy: 0.1562 - loss: 3.9611
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0819 - loss: 4.4602  
[1m 72/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0774 - loss: 4.3496
[1m114/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0777 - loss: 4.2638
[1m154/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0803 - loss: 4.1816
[1m192/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0818 - loss: 4.1091
[1m228/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0827 - loss: 4.0462
[1m267/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0839 - loss: 3.9829
[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0849 - loss: 3.9242
[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0858 - loss: 3.8677
[1m390/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0867 - loss: 3.8171
[1m432/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0877 - loss: 3.7692
[1m474/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0887 - loss: 3.7249
[1m516/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0898 - loss: 3.6842
[1m558/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.0908 - loss: 3.6465
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.0914 - loss: 3.6245
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 9ms/step - accuracy: 0.0914 - loss: 3.6237 - val_accuracy: 0.1508 - val_loss: 2.4841
Epoch 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.2531
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1906 - loss: 2.2903 - val_accuracy: 0.2313 - val_loss: 2.0977
Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.1615
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2080 - loss: 2.1839  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2096 - loss: 2.1894
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[1m151/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2118 - loss: 2.1937
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[1m339/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2130 - loss: 2.1960
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[1m533/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2137 - loss: 2.1947
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2139 - loss: 2.1937 - val_accuracy: 0.2466 - val_loss: 2.0733
Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 1.9569
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2294 - loss: 2.0897  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2327 - loss: 2.0998
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Epoch 8/125

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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.4998
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3036 - loss: 1.9604  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 1.9758
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2605 - loss: 2.0373
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.7114
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 1.9562  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2548 - loss: 1.9753
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7446
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2755 - loss: 1.9503  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2735 - loss: 1.9823 - val_accuracy: 0.2938 - val_loss: 1.9213
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5000 - loss: 1.5765
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2846 - loss: 1.9276  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2799 - loss: 1.9392
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[1m348/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2702 - loss: 1.9700
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[1m580/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2712 - loss: 1.9695
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2712 - loss: 1.9695 - val_accuracy: 0.3223 - val_loss: 1.8757
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0975
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3072 - loss: 1.9242  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2960 - loss: 1.9234
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2841 - loss: 1.9370
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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1250 - loss: 2.3480
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2905 - loss: 1.9049  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2941 - loss: 1.8940
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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.7079
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2868 - loss: 1.8874  
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Epoch 17/125

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Epoch 18/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3230 - loss: 1.8432  
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.7452
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4375 - loss: 1.7610
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3750 - loss: 1.8228  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.8303
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[1m568/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3364 - loss: 1.8240
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3361 - loss: 1.8240 - val_accuracy: 0.3756 - val_loss: 1.7213
Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.8027
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3801 - loss: 1.7391  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.7729
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[1m230/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.8000
[1m266/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.8017
[1m306/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.8035
[1m344/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.8049
[1m382/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.8062
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3362 - loss: 1.8101 - val_accuracy: 0.3758 - val_loss: 1.7491
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7712
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.7788  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7879
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Epoch 23/125

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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 31ms/step - accuracy: 0.2812 - loss: 1.8595
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.7744  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3502 - loss: 1.7671
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Epoch 25/125

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Epoch 26/125

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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1224
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7624  
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.9350
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9485
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Epoch 31/125

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[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3259 - loss: 1.8027  
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6548
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3718 - loss: 1.7161  
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4688 - loss: 1.4951
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4080 - loss: 1.6158  
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Epoch 34/125

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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.4584
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.6976  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.3456
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3958 - loss: 1.5886  
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[1m528/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3803 - loss: 1.6651
[1m567/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3805 - loss: 1.6654
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3805 - loss: 1.6655 - val_accuracy: 0.3963 - val_loss: 1.6681
Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 1.5784
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[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3667 - loss: 1.6668
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[1m161/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.6693
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.6918
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[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3833 - loss: 1.6874
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[1m329/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3846 - loss: 1.6823
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 568ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 752us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 13: 35.55 [%]
F1-score capturado en la ejecución 13: 33.15 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 794us/step
[1m136/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 743us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 38.26 [%]
Global F1 score (validation) = 37.01 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[8.4872343e-02 5.5324811e-02 1.0389038e-01 ... 7.3221690e-07
  6.4395660e-01 2.0569814e-02]
 [1.8473193e-01 1.4764123e-01 2.0106521e-01 ... 1.0545148e-06
  2.4426755e-01 2.4544781e-02]
 [2.2395055e-01 2.0305163e-01 2.1240005e-01 ... 4.9269515e-06
  7.4659944e-02 2.6763082e-02]
 ...
 [1.5935536e-01 1.6664658e-01 2.0012873e-01 ... 2.3517805e-05
  3.0391786e-01 1.7212175e-02]
 [1.1980333e-01 1.2302254e-01 1.4754497e-01 ... 4.8073512e-04
  4.2079467e-01 4.9472172e-02]
 [1.0174947e-01 1.5773652e-01 1.4607832e-01 ... 2.7776469e-04
  4.0921876e-01 2.2189885e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.94 [%]
Global accuracy score (test) = 34.86 [%]
Global F1 score (train) = 46.05 [%]
Global F1 score (test) = 33.54 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.15      0.02      0.04       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.43      0.34       184
       CAMINAR USUAL SPEED       0.20      0.09      0.12       184
            CAMINAR ZIGZAG       0.14      0.16      0.15       184
          DE PIE BARRIENDO       0.33      0.38      0.35       184
   DE PIE DOBLANDO TOALLAS       0.37      0.29      0.32       184
    DE PIE MOVIENDO LIBROS       0.32      0.12      0.18       184
          DE PIE USANDO PC       0.43      0.58      0.49       184
        FASE REPOSO CON K5       0.32      0.75      0.45       184
INCREMENTAL CICLOERGOMETRO       0.79      0.44      0.56       184
           SENTADO LEYENDO       0.23      0.21      0.22       184
         SENTADO USANDO PC       0.31      0.23      0.26       184
      SENTADO VIENDO LA TV       0.26      0.38      0.31       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.49      0.45       184
                    TROTAR       0.89      0.71      0.79       161

                  accuracy                           0.35      2737
                 macro avg       0.36      0.35      0.34      2737
              weighted avg       0.36      0.35      0.33      2737

2025-11-05 20:42:49.912645: 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`.
2025-11-05 20:42:49.924013: 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 00:00:1762371769.936986  159395 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371769.941081  159395 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371769.950723  159395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371769.950740  159395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371769.950741  159395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371769.950750  159395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:42:49.953924: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371772.211370  159395 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371773.861636  159524 service.cc:152] XLA service 0x74d0d4004630 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371773.861667  159524 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:42:53.894452: 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 00:00:1762371774.066731  159524 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371776.524006  159524 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:27[0m 4s/step - accuracy: 0.0625 - loss: 4.9969
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[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0941 - loss: 3.8431
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Epoch 2/125

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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4833
[1m 31/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1504 - loss: 2.5684  
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0938 - loss: 2.5388
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1710 - loss: 2.4510  
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Epoch 5/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2010 - loss: 2.2707 - val_accuracy: 0.2631 - val_loss: 2.1023
Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0909
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2062 - loss: 2.2094  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2081 - loss: 2.2129
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[1m340/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2138 - loss: 2.1996
[1m381/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2140 - loss: 2.1981
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2153 - loss: 2.1908 - val_accuracy: 0.2598 - val_loss: 2.0386
Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.0866
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2092 - loss: 2.1456  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2111 - loss: 2.1483
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3438 - loss: 2.0970
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.0682
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2503 - loss: 2.0682 - val_accuracy: 0.2707 - val_loss: 1.9720
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1504
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2516 - loss: 2.0486  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0579
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[1m384/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.0536
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[1m542/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2511 - loss: 2.0531
[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.0527
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 1.9615
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2401 - loss: 2.0346  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2457 - loss: 2.0374
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 2.1152
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2714 - loss: 1.9838 - val_accuracy: 0.2964 - val_loss: 1.8916
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 1.9106
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3008 - loss: 1.9141  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2936 - loss: 1.9242
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[1m352/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9594
[1m392/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2772 - loss: 1.9605
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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5000 - loss: 2.0558
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3093 - loss: 1.9881  
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Epoch 14/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2818 - loss: 1.9267 - val_accuracy: 0.3312 - val_loss: 1.8251
Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.7695
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3030 - loss: 1.8615  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.8694
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Epoch 16/125

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[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 1.9850  
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Epoch 17/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3082 - loss: 1.8791 - val_accuracy: 0.3223 - val_loss: 1.7996
Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.9773
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3203 - loss: 1.8359  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3236 - loss: 1.8353
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3182 - loss: 1.8499
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Epoch 19/125

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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.9575
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3197 - loss: 1.8469  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3281 - loss: 1.8354
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.8728
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.7847  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3274 - loss: 1.8064
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[1m573/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3254 - loss: 1.8283
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3254 - loss: 1.8283 - val_accuracy: 0.3525 - val_loss: 1.7865
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.4375 - loss: 1.5772
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.7396  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.7708
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[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.8088
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3341 - loss: 1.8088 - val_accuracy: 0.3375 - val_loss: 1.7747
Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9988
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3304 - loss: 1.8564  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3304 - loss: 1.8360
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[1m314/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.7961
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[1m391/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7963
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[1m509/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7971
[1m546/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7973
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3398 - loss: 1.7972 - val_accuracy: 0.3674 - val_loss: 1.7350
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.7191
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3290 - loss: 1.7724  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3286 - loss: 1.7904
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.9089
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.7809  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.7686
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3406 - loss: 1.7738 - val_accuracy: 0.3560 - val_loss: 1.7366
Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 1.6310
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3126 - loss: 1.7720  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.7653
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[1m304/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7701
[1m339/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7704
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[1m574/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7698
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.2812 - loss: 1.8262
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3294 - loss: 1.7484  
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7527
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3768 - loss: 1.7188  
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3750 - loss: 1.7328
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3684 - loss: 1.6603  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3660 - loss: 1.6823
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Epoch 31/125

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Epoch 32/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3697 - loss: 1.6843  
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Epoch 33/125

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Epoch 34/125

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Epoch 35/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3812 - loss: 1.7049  
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Epoch 36/125

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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.7904
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3842 - loss: 1.6721 - val_accuracy: 0.3802 - val_loss: 1.7034
Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.5960
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3886 - loss: 1.6669  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3886 - loss: 1.6646
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[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3934 - loss: 1.6465
[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3933 - loss: 1.6473
[1m384/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3932 - loss: 1.6479
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Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.5803
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3932 - loss: 1.6363  
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Saved model to disk.

Accuracy capturado en la ejecución 14: 34.86 [%]
F1-score capturado en la ejecución 14: 33.54 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 67/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 764us/step
[1m137/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 745us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.43 [%]
Global F1 score (validation) = 37.56 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[7.57726654e-02 1.02289937e-01 9.66786295e-02 ... 2.80863020e-07
  6.47373021e-01 5.54614281e-03]
 [1.78679645e-01 2.63419449e-01 2.26552993e-01 ... 2.95378896e-07
  1.49822295e-01 3.18068429e-03]
 [1.67823792e-01 2.99581617e-01 2.27561429e-01 ... 7.10280801e-05
  9.33185890e-02 6.74872007e-03]
 ...
 [1.49579704e-01 2.10295722e-01 1.67631432e-01 ... 2.57231732e-05
  3.11079532e-01 1.52631095e-02]
 [1.07204944e-01 1.46268249e-01 1.25101119e-01 ... 5.06386068e-03
  2.99015343e-01 4.35785875e-02]
 [1.13686748e-01 2.95231730e-01 1.71059534e-01 ... 1.16003930e-05
  2.93224901e-01 5.41785453e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.36 [%]
Global accuracy score (test) = 37.08 [%]
Global F1 score (train) = 45.71 [%]
Global F1 score (test) = 35.39 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.35      0.04      0.08       184
 CAMINAR CON MÓVIL O LIBRO       0.23      0.58      0.33       184
       CAMINAR USUAL SPEED       0.25      0.03      0.06       184
            CAMINAR ZIGZAG       0.15      0.06      0.09       184
          DE PIE BARRIENDO       0.40      0.40      0.40       184
   DE PIE DOBLANDO TOALLAS       0.42      0.38      0.40       184
    DE PIE MOVIENDO LIBROS       0.37      0.34      0.35       184
          DE PIE USANDO PC       0.48      0.46      0.47       184
        FASE REPOSO CON K5       0.30      0.88      0.45       184
INCREMENTAL CICLOERGOMETRO       0.93      0.36      0.52       184
           SENTADO LEYENDO       0.36      0.32      0.34       184
         SENTADO USANDO PC       0.21      0.20      0.20       184
      SENTADO VIENDO LA TV       0.40      0.38      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.52      0.47       184
                    TROTAR       0.95      0.66      0.78       161

                  accuracy                           0.37      2737
                 macro avg       0.41      0.37      0.35      2737
              weighted avg       0.41      0.37      0.35      2737

2025-11-05 20:43:53.027617: 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`.
2025-11-05 20:43:53.038988: 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 00:00:1762371833.052188  164333 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371833.056532  164333 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371833.066355  164333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371833.066374  164333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371833.066376  164333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371833.066377  164333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:43:53.069555: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371835.348130  164333 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371837.000039  164465 service.cc:152] XLA service 0x7e81e801dc10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371837.000064  164465 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:43:57.032999: 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 00:00:1762371837.199780  164465 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371839.621398  164465 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 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.3354
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1808 - loss: 2.3448  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1829 - loss: 2.3474
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.3596
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2119 - loss: 2.2359  
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Epoch 7/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2229 - loss: 2.1633 - val_accuracy: 0.2579 - val_loss: 2.0263
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0289
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2341 - loss: 2.1312  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2322 - loss: 2.1348
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[1m271/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2319 - loss: 2.1314
[1m310/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2317 - loss: 2.1312
[1m343/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2316 - loss: 2.1307
[1m384/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2318 - loss: 2.1301
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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7619
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[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.0414
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0344
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2576 - loss: 2.0308  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2609 - loss: 2.0272 - val_accuracy: 0.3016 - val_loss: 1.9064
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 27ms/step - accuracy: 0.2500 - loss: 1.9668
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2355 - loss: 2.0167  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.0014
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2516 - loss: 1.9945
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[1m306/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2577 - loss: 1.9920
[1m343/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2584 - loss: 1.9922
[1m382/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2590 - loss: 1.9925
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[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2617 - loss: 1.9930
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Epoch 12/125

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Epoch 13/125

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Epoch 14/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2548 - loss: 1.9901  
[1m 72/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2633 - loss: 1.9696
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Epoch 15/125

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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7699
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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3750 - loss: 1.7385
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3336 - loss: 1.8419  
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Epoch 18/125

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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 2.0687
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[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3165 - loss: 1.8928
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.1875 - loss: 1.9852
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.8445  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3207 - loss: 1.8474
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.8021
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7780  
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4688 - loss: 1.6013
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.8188  
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[1m315/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7880
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8834
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3563 - loss: 1.7711  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.7590
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3475 - loss: 1.7721 - val_accuracy: 0.3702 - val_loss: 1.7073
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.3125 - loss: 1.8569
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3357 - loss: 1.7656  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3348 - loss: 1.7763
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3432 - loss: 1.7747 - val_accuracy: 0.3748 - val_loss: 1.6869
Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.6962
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3609 - loss: 1.7447  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7481
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[1m543/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.7635
[1m583/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.7632
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3493 - loss: 1.7632 - val_accuracy: 0.3717 - val_loss: 1.6883
Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3944
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3864 - loss: 1.6744  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3728 - loss: 1.7057
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Epoch 27/125

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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6537
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3598 - loss: 1.7184  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3606 - loss: 1.7195
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.7244
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Epoch 30/125

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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8611
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3830 - loss: 1.6754  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3807 - loss: 1.6756
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4793
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Epoch 33/125

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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6174
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3831 - loss: 1.6783  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3798 - loss: 1.6815
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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5312 - loss: 1.5580
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3961 - loss: 1.7049  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.6543
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.4375 - loss: 1.6001
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3832 - loss: 1.6028  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3833 - loss: 1.6214
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[1m398/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3804 - loss: 1.6576
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.7450
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4018 - loss: 1.6515  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3960 - loss: 1.6523
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Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.6429
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.6883  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.6843
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3762 - loss: 1.6688 - val_accuracy: 0.3920 - val_loss: 1.6676
Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.6868
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3934 - loss: 1.6839  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3882 - loss: 1.6798
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[1m298/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3881 - loss: 1.6669
[1m338/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3879 - loss: 1.6665
[1m375/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3876 - loss: 1.6661
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[1m531/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3869 - loss: 1.6645
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Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4688 - loss: 1.5189
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3816 - loss: 1.5842  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3775 - loss: 1.6149
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[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 762us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 15: 37.08 [%]
F1-score capturado en la ejecución 15: 35.39 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 67/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 763us/step
[1m143/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 709us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.45 [%]
Global F1 score (validation) = 36.71 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[7.6486260e-02 5.0092101e-02 8.6323977e-02 ... 4.5429333e-06
  6.4991450e-01 5.5791963e-02]
 [1.5267628e-01 1.8108343e-01 1.8955214e-01 ... 9.4467805e-06
  2.7782321e-01 2.9107174e-02]
 [2.1213667e-01 3.0680105e-01 2.3483545e-01 ... 5.6284658e-05
  3.6438894e-02 4.9700299e-03]
 ...
 [2.1485719e-01 1.9923651e-01 2.4688920e-01 ... 4.7429303e-08
  1.2448062e-01 6.6209412e-03]
 [1.2279384e-01 1.3990518e-01 1.4642707e-01 ... 4.2709475e-04
  4.1021746e-01 3.4994856e-02]
 [1.0115973e-01 1.7578107e-01 1.4646162e-01 ... 2.6357916e-04
  3.7594378e-01 1.6938707e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.84 [%]
Global accuracy score (test) = 36.35 [%]
Global F1 score (train) = 46.27 [%]
Global F1 score (test) = 34.96 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.34      0.22      0.27       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.48      0.34       184
       CAMINAR USUAL SPEED       0.27      0.15      0.19       184
            CAMINAR ZIGZAG       0.11      0.03      0.04       184
          DE PIE BARRIENDO       0.15      0.10      0.12       184
   DE PIE DOBLANDO TOALLAS       0.33      0.45      0.38       184
    DE PIE MOVIENDO LIBROS       0.25      0.14      0.17       184
          DE PIE USANDO PC       0.32      0.60      0.42       184
        FASE REPOSO CON K5       0.36      0.75      0.49       184
INCREMENTAL CICLOERGOMETRO       0.86      0.48      0.62       184
           SENTADO LEYENDO       0.36      0.36      0.36       184
         SENTADO USANDO PC       0.31      0.20      0.24       184
      SENTADO VIENDO LA TV       0.36      0.38      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.48      0.54      0.51       184
                    TROTAR       0.92      0.60      0.73       161

                  accuracy                           0.36      2737
                 macro avg       0.38      0.37      0.35      2737
              weighted avg       0.37      0.36      0.35      2737

2025-11-05 20:44:57.930338: 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`.
2025-11-05 20:44:57.941565: 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 00:00:1762371897.954491  169490 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371897.958671  169490 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371897.968389  169490 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371897.968406  169490 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371897.968407  169490 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371897.968408  169490 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:44:57.971521: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371900.224333  169490 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371901.870268  169620 service.cc:152] XLA service 0x703ad800b6f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371901.870296  169620 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:45:01.903568: 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 00:00:1762371902.068503  169620 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371904.466464  169620 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 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0938 - loss: 2.3237
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1666 - loss: 2.3343  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1786 - loss: 2.3279
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1856 - loss: 2.3358
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1793
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2144 - loss: 2.2305  
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Epoch 7/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2248 - loss: 2.1417 - val_accuracy: 0.2636 - val_loss: 2.0021
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0717
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2636 - loss: 2.0521  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2566 - loss: 2.0618
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[1m381/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2435 - loss: 2.0764
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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2427
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2324 - loss: 2.1428  
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8985
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2621 - loss: 2.0297 - val_accuracy: 0.3136 - val_loss: 1.9042
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0099
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2841 - loss: 1.9524  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2838 - loss: 1.9720
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[1m317/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2722 - loss: 1.9869
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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3750 - loss: 2.0225
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3149 - loss: 2.0153  
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Epoch 13/125

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Epoch 14/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2972 - loss: 1.9047  
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Epoch 15/125

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Epoch 16/125

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Epoch 17/125

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[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3048 - loss: 1.8706  
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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4062 - loss: 1.9854
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3259 - loss: 1.8310  
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.7654
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.5000 - loss: 1.5502
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3258 - loss: 1.8211  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3237 - loss: 1.8195
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Epoch 21/125

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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9278
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3086 - loss: 1.9045  
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.6304
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3127 - loss: 1.8122  
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[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3293 - loss: 1.8045
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3293 - loss: 1.8045 - val_accuracy: 0.3573 - val_loss: 1.7429
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.6321
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7422  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7687
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.7846
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3408 - loss: 1.7958 - val_accuracy: 0.3843 - val_loss: 1.7324
Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0067
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3290 - loss: 1.8137  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3367 - loss: 1.7954
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[1m270/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7877
[1m308/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7860
[1m346/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7851
[1m381/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.7846
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[1m454/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3385 - loss: 1.7833
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[1m528/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7821
[1m568/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7816
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3390 - loss: 1.7814 - val_accuracy: 0.3691 - val_loss: 1.7157
Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1109
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3244 - loss: 1.7907  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3334 - loss: 1.7881
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Epoch 27/125

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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4809
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7238  
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.6788
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.7227  
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7958
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3362 - loss: 1.7632  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3592 - loss: 1.7362 - val_accuracy: 0.3706 - val_loss: 1.7166
Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.5021
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.6823  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.6933
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[1m313/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.7222
[1m352/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.7227
[1m390/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.7231
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3626 - loss: 1.7240 - val_accuracy: 0.3956 - val_loss: 1.6795
Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.7172
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3973 - loss: 1.6504  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3838 - loss: 1.6692
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Epoch 33/125

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Epoch 34/125

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Epoch 35/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3783 - loss: 1.6981 - val_accuracy: 0.3693 - val_loss: 1.6921
Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5312 - loss: 1.6693
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[1m583/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3715 - loss: 1.6944
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5312 - loss: 1.4333
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3900 - loss: 1.7060  
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 567ms/step
[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 932us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 36.35 [%]
F1-score capturado en la ejecución 16: 34.96 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 65/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 787us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 756us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 38.02 [%]
Global F1 score (validation) = 36.59 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[7.94946700e-02 7.49640316e-02 1.14490718e-01 ... 1.11540345e-07
  6.48974717e-01 5.84453624e-03]
 [1.70008287e-01 2.19503999e-01 2.52261728e-01 ... 2.05250288e-07
  1.88902780e-01 2.68142601e-03]
 [1.85399830e-01 3.03578973e-01 2.46965021e-01 ... 7.48939510e-06
  7.65691549e-02 3.77547345e-03]
 ...
 [2.08479881e-01 1.61166608e-01 2.48368099e-01 ... 6.90037893e-08
  2.14401439e-01 7.51426071e-03]
 [1.25808269e-01 1.69884533e-01 1.65955931e-01 ... 5.74149235e-05
  3.77309173e-01 1.37574915e-02]
 [1.28052279e-01 1.82568789e-01 1.70702264e-01 ... 2.91917440e-05
  3.48959565e-01 1.23523148e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.66 [%]
Global accuracy score (test) = 35.04 [%]
Global F1 score (train) = 45.89 [%]
Global F1 score (test) = 33.46 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.15      0.19       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.44      0.33       184
       CAMINAR USUAL SPEED       0.23      0.23      0.23       184
            CAMINAR ZIGZAG       0.00      0.00      0.00       184
          DE PIE BARRIENDO       0.21      0.24      0.22       184
   DE PIE DOBLANDO TOALLAS       0.28      0.41      0.33       184
    DE PIE MOVIENDO LIBROS       0.40      0.29      0.33       184
          DE PIE USANDO PC       0.33      0.59      0.42       184
        FASE REPOSO CON K5       0.39      0.82      0.52       184
INCREMENTAL CICLOERGOMETRO       0.86      0.42      0.57       184
           SENTADO LEYENDO       0.37      0.32      0.34       184
         SENTADO USANDO PC       0.08      0.04      0.05       184
      SENTADO VIENDO LA TV       0.30      0.25      0.27       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.48      0.46       184
                    TROTAR       0.94      0.61      0.74       161

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

2025-11-05 20:45:58.999824: 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`.
2025-11-05 20:45:59.012334: 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 00:00:1762371959.025807  174239 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762371959.029986  174239 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762371959.039812  174239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371959.039829  174239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371959.039831  174239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762371959.039833  174239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:45:59.042964: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762371961.348454  174239 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762371963.007650  174358 service.cc:152] XLA service 0x7c4a4c0051a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762371963.007686  174358 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:46:03.041391: 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 00:00:1762371963.212923  174358 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762371965.658336  174358 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:27[0m 4s/step - accuracy: 0.0938 - loss: 4.3408
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[1m 67/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0780 - loss: 4.3260
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[1m221/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0870 - loss: 4.0245
[1m261/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0882 - loss: 3.9630
[1m300/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0892 - loss: 3.9073
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1729 - loss: 2.4168 - val_accuracy: 0.2170 - val_loss: 2.2079
Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4612
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1880 - loss: 2.3424  
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[1m218/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1931 - loss: 2.3137
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[1m291/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.3093
[1m329/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.3081
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1949 - loss: 2.2988 - val_accuracy: 0.2350 - val_loss: 2.1399
Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.2405
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2096 - loss: 2.1851  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2082 - loss: 2.1950
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[1m145/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2083 - loss: 2.2063
[1m181/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2081 - loss: 2.2077
[1m220/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2079 - loss: 2.2086
[1m260/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2077 - loss: 2.2094
[1m300/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.2093
[1m337/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2080 - loss: 2.2089
[1m375/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.2080
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[1m530/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2096 - loss: 2.2039
[1m568/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.2029
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.1834
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.1966  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2203 - loss: 2.1773
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Epoch 8/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2320 - loss: 2.0841 - val_accuracy: 0.2533 - val_loss: 1.9981
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0680
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2654 - loss: 2.0300  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2619 - loss: 2.0371
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[1m152/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2591 - loss: 2.0405
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[1m306/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0412
[1m342/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0417
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 2.2464
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2277 - loss: 2.0514  
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Epoch 11/125

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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.0361
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2641 - loss: 1.9748  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2754 - loss: 1.9636
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Epoch 13/125

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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.0374
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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.2812 - loss: 2.0270
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2944 - loss: 1.9552  
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Epoch 16/125

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Epoch 17/125

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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.3750 - loss: 1.7113
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3222 - loss: 1.8174  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3106 - loss: 1.8599 - val_accuracy: 0.3513 - val_loss: 1.7793
Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.0850
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.8605  
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[1m582/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.8540
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3140 - loss: 1.8540 - val_accuracy: 0.3451 - val_loss: 1.7780
Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1077
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2883 - loss: 1.9072  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2960 - loss: 1.8934
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[1m531/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3182 - loss: 1.8505
[1m567/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3188 - loss: 1.8492
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3191 - loss: 1.8486 - val_accuracy: 0.3512 - val_loss: 1.7604
Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7601
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3104 - loss: 1.8109  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3164 - loss: 1.8200
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[1m528/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3262 - loss: 1.8236
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3266 - loss: 1.8231 - val_accuracy: 0.3735 - val_loss: 1.7392
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.4062 - loss: 1.9499
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3597 - loss: 1.8091  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3569 - loss: 1.7861
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[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.7871
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3404 - loss: 1.7872 - val_accuracy: 0.3708 - val_loss: 1.7556
Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7765
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3260 - loss: 1.8623  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3336 - loss: 1.8406
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[1m298/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.8147
[1m337/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.8129
[1m375/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.8115
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[1m522/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.8081
[1m563/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.8074
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3416 - loss: 1.8071 - val_accuracy: 0.3482 - val_loss: 1.7260
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7245
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3626 - loss: 1.7655  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3581 - loss: 1.7681
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Epoch 25/125

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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1404
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3403 - loss: 1.7853  
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.3125 - loss: 1.7113
[1m 31/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3470 - loss: 1.7570  
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 1.9860
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3593 - loss: 1.7379 - val_accuracy: 0.3689 - val_loss: 1.6878
Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.7362
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.7632  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.7578
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[1m313/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.7420
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9575
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.6956  
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Epoch 31/125

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Epoch 32/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.7558  
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Epoch 33/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3828 - loss: 1.7051  
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Epoch 34/125

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Epoch 35/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.7216  
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 573ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 828us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 17: 35.04 [%]
F1-score capturado en la ejecución 17: 33.46 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 67/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 762us/step
[1m135/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 749us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.71 [%]
Global F1 score (validation) = 35.94 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[7.81928152e-02 1.53431222e-01 1.51429817e-01 ... 5.76256070e-07
  5.09310186e-01 3.89059354e-03]
 [1.58942625e-01 3.03125799e-01 2.64075160e-01 ... 2.42259489e-07
  6.47222027e-02 1.59728539e-03]
 [1.49674147e-01 2.52395868e-01 2.00141355e-01 ... 8.55797029e-04
  6.40557483e-02 9.90964379e-03]
 ...
 [1.26148880e-01 1.95376292e-01 2.16641277e-01 ... 9.35781600e-07
  3.17481697e-01 1.00957602e-02]
 [1.14166953e-01 1.42352000e-01 1.43701643e-01 ... 4.17330209e-03
  2.54949182e-01 4.02619354e-02]
 [7.63249993e-02 1.18459836e-01 1.13175951e-01 ... 1.23193813e-03
  3.40468377e-01 4.60289530e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 46.79 [%]
Global accuracy score (test) = 32.81 [%]
Global F1 score (train) = 44.77 [%]
Global F1 score (test) = 31.34 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.33      0.01      0.02       184
 CAMINAR CON MÓVIL O LIBRO       0.23      0.51      0.31       184
       CAMINAR USUAL SPEED       0.20      0.14      0.17       184
            CAMINAR ZIGZAG       0.21      0.05      0.09       184
          DE PIE BARRIENDO       0.19      0.29      0.23       184
   DE PIE DOBLANDO TOALLAS       0.38      0.38      0.38       184
    DE PIE MOVIENDO LIBROS       0.28      0.12      0.17       184
          DE PIE USANDO PC       0.31      0.60      0.41       184
        FASE REPOSO CON K5       0.41      0.66      0.51       184
INCREMENTAL CICLOERGOMETRO       0.73      0.33      0.46       184
           SENTADO LEYENDO       0.30      0.36      0.33       184
         SENTADO USANDO PC       0.15      0.21      0.18       184
      SENTADO VIENDO LA TV       0.45      0.18      0.26       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.49      0.45       184
                    TROTAR       0.94      0.61      0.74       161

                  accuracy                           0.33      2737
                 macro avg       0.37      0.33      0.31      2737
              weighted avg       0.36      0.33      0.31      2737

2025-11-05 20:46:58.523970: 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`.
2025-11-05 20:46:58.535536: 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 00:00:1762372018.549419  178798 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372018.553645  178798 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372018.563999  178798 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372018.564019  178798 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372018.564020  178798 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372018.564021  178798 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:46:58.567291: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372020.844680  178798 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372022.502557  178919 service.cc:152] XLA service 0x7813d8004580 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372022.502591  178919 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:47:02.538075: 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 00:00:1762372022.702599  178919 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372025.134109  178919 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:12[0m 4s/step - accuracy: 0.0312 - loss: 3.7823
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0550 - loss: 4.2130  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0664 - loss: 4.1835
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[1m192/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0808 - loss: 3.9887
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0938 - loss: 2.4983
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.2188 - loss: 2.1583
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Epoch 7/125

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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.2500 - loss: 2.1572
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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 1.9346
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2186 - loss: 2.0682  
[1m 72/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2169 - loss: 2.0805
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[1m536/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2366 - loss: 2.0682
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8824
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2432 - loss: 2.0610  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0647
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2529 - loss: 2.0397 - val_accuracy: 0.2897 - val_loss: 1.8994
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.2500 - loss: 2.4021
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2425 - loss: 2.0572  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2524 - loss: 2.0271
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[1m355/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2671 - loss: 2.0037
[1m390/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2674 - loss: 2.0028
[1m427/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2677 - loss: 2.0020
[1m467/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2678 - loss: 2.0015
[1m506/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 2.0013
[1m540/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 2.0011
[1m582/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2680 - loss: 2.0009
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2680 - loss: 2.0009 - val_accuracy: 0.2884 - val_loss: 1.9219
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 1.8359
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2701 - loss: 1.9708  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2745 - loss: 1.9675
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 1.9707
[1m238/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2793 - loss: 1.9704
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2803 - loss: 1.9736 - val_accuracy: 0.3008 - val_loss: 1.8825
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0296
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3010 - loss: 1.9552  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2912 - loss: 1.9398 - val_accuracy: 0.2977 - val_loss: 1.8860
Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.9136
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2685 - loss: 1.9841  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2726 - loss: 1.9706
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[1m543/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2818 - loss: 1.9370
[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2822 - loss: 1.9364
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2822 - loss: 1.9363 - val_accuracy: 0.3177 - val_loss: 1.8406
Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7609
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2473 - loss: 1.9185  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 1.9069
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Epoch 16/125

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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.6245
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3067 - loss: 1.8687  
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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0625 - loss: 2.1890
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3050 - loss: 1.8589  
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7918
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3207 - loss: 1.8377 - val_accuracy: 0.3378 - val_loss: 1.7762
Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1530
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3226 - loss: 1.8912  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3244 - loss: 1.8739
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7682
[1m 31/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3337 - loss: 1.8286  
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.8844
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3262 - loss: 1.8164 - val_accuracy: 0.3510 - val_loss: 1.7546
Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.6984
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.7933  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3531 - loss: 1.8025
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5000 - loss: 1.6328
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.7803  
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Epoch 25/125

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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 2.1172
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3333 - loss: 1.8285  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7902
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Epoch 27/125

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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.9488
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 1.7848
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3373 - loss: 1.7473  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7289
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Epoch 30/125

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Epoch 31/125

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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.3750 - loss: 1.7829
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3677 - loss: 1.7327  
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.6679
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.6984
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.6926  
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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.4062 - loss: 1.6781
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4041 - loss: 1.6587  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3939 - loss: 1.6751
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[1m502/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3788 - loss: 1.6937
[1m539/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3783 - loss: 1.6937
[1m578/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3779 - loss: 1.6936
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3778 - loss: 1.6936 - val_accuracy: 0.3928 - val_loss: 1.6961
Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.5249
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3847 - loss: 1.6382  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3894 - loss: 1.6435
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[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3843 - loss: 1.6682
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3842 - loss: 1.6683 - val_accuracy: 0.3967 - val_loss: 1.6989
Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2188 - loss: 1.9014
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.6685  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3612 - loss: 1.6555
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[1m231/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3705 - loss: 1.6685
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[1m343/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3744 - loss: 1.6699
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[1m539/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3787 - loss: 1.6680
[1m578/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3791 - loss: 1.6678
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 554ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 811us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 32.81 [%]
F1-score capturado en la ejecución 18: 31.34 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 61/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 844us/step
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 766us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 38.67 [%]
Global F1 score (validation) = 36.41 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[6.8793885e-02 5.6050450e-02 8.9615323e-02 ... 1.0783763e-06
  6.8605685e-01 1.7887980e-02]
 [1.9686928e-01 1.8537766e-01 2.4010113e-01 ... 4.2245642e-07
  1.4775516e-01 5.3639547e-03]
 [2.0692566e-01 2.1361984e-01 2.1742523e-01 ... 2.9380850e-05
  1.0864455e-01 1.2373759e-02]
 ...
 [1.3991748e-01 1.9718970e-01 1.8678166e-01 ... 5.5165441e-07
  3.3489895e-01 8.5708862e-03]
 [1.2743138e-01 1.5135625e-01 1.3767640e-01 ... 1.2757421e-03
  3.7021825e-01 2.9903082e-02]
 [1.3717766e-01 1.7504761e-01 1.4880043e-01 ... 9.8165692e-05
  3.4795544e-01 3.2221951e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 46.49 [%]
Global accuracy score (test) = 34.93 [%]
Global F1 score (train) = 43.53 [%]
Global F1 score (test) = 32.88 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.01      0.02       184
 CAMINAR CON MÓVIL O LIBRO       0.23      0.28      0.25       184
       CAMINAR USUAL SPEED       0.23      0.06      0.10       184
            CAMINAR ZIGZAG       0.19      0.28      0.22       184
          DE PIE BARRIENDO       0.28      0.35      0.31       184
   DE PIE DOBLANDO TOALLAS       0.35      0.30      0.33       184
    DE PIE MOVIENDO LIBROS       0.31      0.37      0.34       184
          DE PIE USANDO PC       0.46      0.47      0.46       184
        FASE REPOSO CON K5       0.32      0.75      0.45       184
INCREMENTAL CICLOERGOMETRO       0.94      0.37      0.53       184
           SENTADO LEYENDO       0.47      0.12      0.20       184
         SENTADO USANDO PC       0.15      0.07      0.10       184
      SENTADO VIENDO LA TV       0.31      0.59      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.62      0.49       184
                    TROTAR       0.88      0.62      0.73       161

                  accuracy                           0.35      2737
                 macro avg       0.38      0.35      0.33      2737
              weighted avg       0.38      0.35      0.33      2737

2025-11-05 20:47:59.582118: 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`.
2025-11-05 20:47:59.593283: 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 00:00:1762372079.606174  183543 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372079.610254  183543 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372079.620008  183543 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372079.620024  183543 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372079.620026  183543 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372079.620027  183543 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:47:59.623162: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372081.890886  183543 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372083.529447  183674 service.cc:152] XLA service 0x713d3401d660 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372083.529477  183674 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:48:03.564102: 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 00:00:1762372083.736705  183674 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372086.246808  183674 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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[1m512/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0949 - loss: 3.6808
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.6135
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Epoch 5/125

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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1875 - loss: 2.1729
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2065 - loss: 2.2089  
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1534
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2030 - loss: 2.1399  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2115 - loss: 2.1298
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Epoch 8/125

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[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2481 - loss: 2.1114  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2475 - loss: 2.0991
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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.1144
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2624 - loss: 2.0244  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2577 - loss: 2.0406
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[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2572 - loss: 2.0463
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2572 - loss: 2.0461 - val_accuracy: 0.2635 - val_loss: 1.9265
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.3125 - loss: 1.9974
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2873 - loss: 1.9665  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2804 - loss: 1.9760
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2675 - loss: 2.0030 - val_accuracy: 0.2962 - val_loss: 1.8971
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.0239
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 1.9841  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 1.9932
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2625 - loss: 1.9835
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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7771
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2894 - loss: 1.9135  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2875 - loss: 1.9250
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[1m538/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2815 - loss: 1.9414
[1m572/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2815 - loss: 1.9417
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2815 - loss: 1.9418 - val_accuracy: 0.3147 - val_loss: 1.8477
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4375 - loss: 1.6128
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2992 - loss: 1.9137  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2856 - loss: 1.9492
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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7909
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3127 - loss: 1.8767  
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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9579
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2883 - loss: 1.9267  
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Epoch 17/125

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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 1.8428
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4688 - loss: 1.7766
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[1m512/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3307 - loss: 1.8305
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0258
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.1078
[1m 42/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3033 - loss: 1.8816  
[1m 83/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3172 - loss: 1.8504
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8778
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3535 - loss: 1.7895  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7848
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 30ms/step - accuracy: 0.3125 - loss: 1.8459
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7669  
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8139
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3720 - loss: 1.7688  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3714 - loss: 1.7588
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[1m583/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7622
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.6067
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7099  
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Epoch 26/125

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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8603
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3327 - loss: 1.7663  
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.5312 - loss: 1.5037
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Epoch 29/125

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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.6113
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3686 - loss: 1.7655  
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9664
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3773 - loss: 1.6534  
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Epoch 32/125

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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.7149
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7074  
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 1.9112
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4004 - loss: 1.6711  
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Epoch 35/125

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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.5949
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3790 - loss: 1.6807  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3860 - loss: 1.6651
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[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3850 - loss: 1.6696
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.5625 - loss: 1.7050
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4375 - loss: 1.6356
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3980 - loss: 1.6613  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3912 - loss: 1.6680
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3886 - loss: 1.6497 - val_accuracy: 0.4013 - val_loss: 1.6599
Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 2.0724
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3849 - loss: 1.6795  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.6652
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[1m185/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3874 - loss: 1.6593
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[1m262/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3880 - loss: 1.6585
[1m302/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3887 - loss: 1.6566
[1m340/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3890 - loss: 1.6548
[1m380/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3894 - loss: 1.6533
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[1m539/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3900 - loss: 1.6496
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Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.6828
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3830 - loss: 1.6034  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3830 - loss: 1.6269
[1m116/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3853 - loss: 1.6352
[1m152/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3861 - loss: 1.6388
[1m193/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3868 - loss: 1.6417
[1m233/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3874 - loss: 1.6433
[1m274/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3879 - loss: 1.6440
[1m313/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3880 - loss: 1.6445
[1m354/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3887 - loss: 1.6442
[1m389/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3891 - loss: 1.6438
[1m431/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3895 - loss: 1.6434
[1m470/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3895 - loss: 1.6434
[1m510/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3895 - loss: 1.6431
[1m551/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3896 - loss: 1.6426
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3897 - loss: 1.6423 - val_accuracy: 0.3758 - val_loss: 1.6485
Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.5887
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3680 - loss: 1.6499  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3703 - loss: 1.6488
[1m117/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3725 - loss: 1.6490
[1m158/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3753 - loss: 1.6481
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[1m315/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3799 - loss: 1.6443
[1m354/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3804 - loss: 1.6442
[1m394/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3810 - loss: 1.6438
[1m434/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3816 - loss: 1.6431
[1m475/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3822 - loss: 1.6426
[1m511/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3826 - loss: 1.6424
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3831 - loss: 1.6427 - val_accuracy: 0.3882 - val_loss: 1.6692
Epoch 42/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.6209
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3900 - loss: 1.6058  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3900 - loss: 1.6141
[1m117/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3887 - loss: 1.6191
[1m158/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3874 - loss: 1.6234
[1m194/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3880 - loss: 1.6254
[1m237/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3889 - loss: 1.6259
[1m277/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3898 - loss: 1.6260
[1m317/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3909 - loss: 1.6253
[1m356/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3919 - loss: 1.6247
[1m396/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3926 - loss: 1.6243
[1m435/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3932 - loss: 1.6237
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[1m514/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3942 - loss: 1.6235
[1m551/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3944 - loss: 1.6236
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3946 - loss: 1.6239 - val_accuracy: 0.3858 - val_loss: 1.6687

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 561ms/step
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 801us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 19: 34.93 [%]
F1-score capturado en la ejecución 19: 32.88 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 67/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 759us/step
[1m132/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 766us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.58 [%]
Global F1 score (validation) = 38.07 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.17239319e-01 1.03719458e-01 1.39814675e-01 ... 1.47818982e-05
  5.03586590e-01 1.44886179e-02]
 [1.85394168e-01 2.58017123e-01 2.63838202e-01 ... 3.75829186e-05
  1.10434085e-01 3.11708613e-03]
 [1.71712965e-01 2.53679365e-01 2.11680606e-01 ... 1.26874459e-03
  5.31859659e-02 1.17352167e-02]
 ...
 [1.71704426e-01 1.84611186e-01 2.05846831e-01 ... 1.21249741e-07
  2.83478111e-01 1.03176693e-02]
 [1.35723561e-01 1.54641539e-01 1.71727881e-01 ... 3.56028351e-04
  3.56851846e-01 2.21865866e-02]
 [9.55849662e-02 1.17907807e-01 1.35148391e-01 ... 8.46172554e-07
  5.65289140e-01 8.66232440e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 48.63 [%]
Global accuracy score (test) = 34.45 [%]
Global F1 score (train) = 48.21 [%]
Global F1 score (test) = 33.24 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.39      0.18      0.25       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.33      0.30       184
       CAMINAR USUAL SPEED       0.23      0.32      0.27       184
            CAMINAR ZIGZAG       0.17      0.02      0.04       184
          DE PIE BARRIENDO       0.27      0.19      0.22       184
   DE PIE DOBLANDO TOALLAS       0.37      0.37      0.37       184
    DE PIE MOVIENDO LIBROS       0.32      0.18      0.23       184
          DE PIE USANDO PC       0.39      0.60      0.47       184
        FASE REPOSO CON K5       0.28      0.86      0.42       184
INCREMENTAL CICLOERGOMETRO       0.83      0.42      0.56       184
           SENTADO LEYENDO       0.24      0.11      0.16       184
         SENTADO USANDO PC       0.15      0.20      0.17       184
      SENTADO VIENDO LA TV       0.41      0.25      0.31       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.54      0.47       184
                    TROTAR       0.93      0.62      0.74       161

                  accuracy                           0.34      2737
                 macro avg       0.38      0.35      0.33      2737
              weighted avg       0.37      0.34      0.33      2737

2025-11-05 20:49:05.524518: 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`.
2025-11-05 20:49:05.535864: 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 00:00:1762372145.549214  188777 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372145.553341  188777 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372145.563182  188777 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372145.563198  188777 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372145.563199  188777 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372145.563207  188777 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:49:05.566325: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372147.842691  188777 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372149.495524  188896 service.cc:152] XLA service 0x7f54c8005610 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372149.495571  188896 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:49:09.536850: 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 00:00:1762372149.707032  188896 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372152.146373  188896 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:28[0m 4s/step - accuracy: 0.0312 - loss: 4.8644
[1m 31/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0787 - loss: 4.3489  
[1m 70/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0834 - loss: 4.2825
[1m108/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0853 - loss: 4.2298
[1m148/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0878 - loss: 4.1698
[1m186/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0893 - loss: 4.1151
[1m226/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0902 - loss: 4.0604
[1m261/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0910 - loss: 4.0135
[1m300/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0920 - loss: 3.9624
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Epoch 2/125

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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.5119
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0625 - loss: 2.5125
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Epoch 5/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1991 - loss: 2.2896 - val_accuracy: 0.2313 - val_loss: 2.1203
Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.3162
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.2281  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2186 - loss: 2.2193
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[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2123 - loss: 2.2046
[1m384/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.2036
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.2163
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2068 - loss: 2.2121  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2182 - loss: 2.1911
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.2644
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[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.0998
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2391 - loss: 2.0996 - val_accuracy: 0.2781 - val_loss: 1.9631
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2433
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2507 - loss: 2.0989  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.0995
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[1m144/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2472 - loss: 2.0949
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[1m261/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2466 - loss: 2.0853
[1m300/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0828
[1m341/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2469 - loss: 2.0804
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Epoch 10/125

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Epoch 11/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2657 - loss: 2.0042 - val_accuracy: 0.2851 - val_loss: 1.9075
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9836
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2765 - loss: 2.0149  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2831 - loss: 2.0033
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Epoch 13/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3002 - loss: 1.9772  
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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8649
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.8824  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3116 - loss: 1.8876
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2966 - loss: 1.9169 - val_accuracy: 0.3262 - val_loss: 1.8261
Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3750 - loss: 1.9033
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2863 - loss: 2.0116  
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Epoch 16/125

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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.1039
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3211 - loss: 1.8861  
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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 1.8379
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Epoch 19/125

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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 27ms/step - accuracy: 0.3750 - loss: 1.9145
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3668 - loss: 1.8210  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.8386
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.5786
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Epoch 22/125

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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6889
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.8378  
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7994
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3369 - loss: 1.8201  
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 1.8508
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.6438
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3645 - loss: 1.7211  
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6209
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3679 - loss: 1.7210  
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9787
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3723 - loss: 1.7046  
[1m 69/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3739 - loss: 1.7087
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[1m295/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3647 - loss: 1.7185
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Epoch 30/125

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Epoch 31/125

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Epoch 32/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3716 - loss: 1.7239  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3718 - loss: 1.7040
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Epoch 33/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3640 - loss: 1.7140  
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Epoch 34/125

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Epoch 35/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3249 - loss: 1.7746  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4062 - loss: 1.5737
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3809 - loss: 1.7008  
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9129
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3765 - loss: 1.6952  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3756 - loss: 1.6903
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5625 - loss: 1.6478
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3858 - loss: 1.6555  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3833 - loss: 1.6603
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3817 - loss: 1.6681 - val_accuracy: 0.3771 - val_loss: 1.6887
Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.9554
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3834 - loss: 1.7130  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3833 - loss: 1.6953
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[1m316/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3822 - loss: 1.6796
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[1m579/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3828 - loss: 1.6736
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3828 - loss: 1.6735 - val_accuracy: 0.3752 - val_loss: 1.6683
Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.8898
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3869 - loss: 1.6929  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3900 - loss: 1.6776
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[1m259/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3936 - loss: 1.6640
[1m299/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3939 - loss: 1.6628
[1m334/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3940 - loss: 1.6623
[1m374/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3940 - loss: 1.6621
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[1m534/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3948 - loss: 1.6594
[1m575/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3948 - loss: 1.6590
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3948 - loss: 1.6590 - val_accuracy: 0.4039 - val_loss: 1.6484
Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.5397
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4306 - loss: 1.5752  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4207 - loss: 1.5899
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[1m234/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4041 - loss: 1.6245
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Epoch 42/125

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Epoch 43/125

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Epoch 44/125

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Epoch 45/125

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Epoch 46/125

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Epoch 47/125

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[1m444/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4070 - loss: 1.6017
[1m485/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4065 - loss: 1.6025
[1m524/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4060 - loss: 1.6034
[1m563/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4055 - loss: 1.6044
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4052 - loss: 1.6051 - val_accuracy: 0.3711 - val_loss: 1.6996
Epoch 48/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.5625 - loss: 1.2096
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Epoch 49/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3750 - loss: 1.6373
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.6203  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3975 - loss: 1.6185
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[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4091 - loss: 1.6050
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 554ms/step
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 743us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 34.45 [%]
F1-score capturado en la ejecución 20: 33.24 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 60/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 859us/step
[1m127/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 801us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 38.82 [%]
Global F1 score (validation) = 37.7 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[6.12144433e-02 4.47418578e-02 8.35710615e-02 ... 2.29353549e-08
  7.38370955e-01 9.90033615e-03]
 [2.27302730e-01 1.91623166e-01 2.73025632e-01 ... 1.14613758e-08
  4.76447195e-02 1.92492153e-03]
 [1.83588177e-01 2.58933216e-01 2.27801919e-01 ... 9.61260666e-05
  8.36010128e-02 1.26326513e-02]
 ...
 [1.49412632e-01 1.59704536e-01 2.63671786e-01 ... 1.61959775e-07
  2.99972236e-01 4.72314982e-03]
 [1.17421366e-01 1.76803544e-01 1.96406335e-01 ... 2.71355966e-04
  3.55171561e-01 1.35740461e-02]
 [1.23513639e-01 1.64650723e-01 1.76453307e-01 ... 6.88388827e-04
  2.78793246e-01 1.84601024e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.51 [%]
Global accuracy score (test) = 35.15 [%]
Global F1 score (train) = 49.2 [%]
Global F1 score (test) = 33.85 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.02      0.04       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.34      0.32       184
       CAMINAR USUAL SPEED       0.24      0.23      0.23       184
            CAMINAR ZIGZAG       0.17      0.16      0.17       184
          DE PIE BARRIENDO       0.32      0.20      0.24       184
   DE PIE DOBLANDO TOALLAS       0.31      0.43      0.37       184
    DE PIE MOVIENDO LIBROS       0.38      0.21      0.27       184
          DE PIE USANDO PC       0.40      0.60      0.48       184
        FASE REPOSO CON K5       0.30      0.86      0.44       184
INCREMENTAL CICLOERGOMETRO       0.90      0.39      0.54       184
           SENTADO LEYENDO       0.30      0.22      0.25       184
         SENTADO USANDO PC       0.20      0.18      0.19       184
      SENTADO VIENDO LA TV       0.38      0.30      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.57      0.48       184
                    TROTAR       0.91      0.60      0.72       161

                  accuracy                           0.35      2737
                 macro avg       0.38      0.35      0.34      2737
              weighted avg       0.38      0.35      0.34      2737

2025-11-05 20:50:18.791188: 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`.
2025-11-05 20:50:18.802642: 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 00:00:1762372218.816244  194747 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372218.820547  194747 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372218.830527  194747 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372218.830545  194747 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372218.830546  194747 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372218.830547  194747 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:50:18.833809: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372221.127980  194747 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372222.782076  194855 service.cc:152] XLA service 0x7b524001ce30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372222.782102  194855 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:50:22.815435: 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 00:00:1762372222.985559  194855 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372225.395664  194855 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 2/125

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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.0625 - loss: 2.7392
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1323 - loss: 2.5769  
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[1m541/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1513 - loss: 2.5239
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.4022
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1827 - loss: 2.4644  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1803 - loss: 2.4533
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Epoch 5/125

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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.1700
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7665
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2258 - loss: 2.1589 - val_accuracy: 0.2618 - val_loss: 2.0187
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9461
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2169 - loss: 2.0971  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2367 - loss: 2.1026 - val_accuracy: 0.2648 - val_loss: 1.9904
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.3851
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2224 - loss: 2.0878  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2365 - loss: 2.0630
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[1m312/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2493 - loss: 2.0457
[1m354/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.0456
[1m389/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0458
[1m429/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0459
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[1m507/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 2.0468
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2502 - loss: 2.0475 - val_accuracy: 0.2679 - val_loss: 1.9784
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.2572
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2729 - loss: 2.0568  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2739 - loss: 2.0398
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[1m192/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2689 - loss: 2.0304
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2642 - loss: 2.0266 - val_accuracy: 0.2990 - val_loss: 1.9237
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5000 - loss: 1.6266
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[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2635 - loss: 1.9998
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[1m568/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0000
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2635 - loss: 1.9998 - val_accuracy: 0.2829 - val_loss: 1.8928
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0882
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2633 - loss: 1.9918  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2671 - loss: 1.9930
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[1m194/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2717 - loss: 1.9882
[1m234/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 1.9879
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[1m313/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2715 - loss: 1.9875
[1m353/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 1.9867
[1m390/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 1.9861
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[1m468/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2719 - loss: 1.9846
[1m501/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2721 - loss: 1.9840
[1m542/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2724 - loss: 1.9830
[1m580/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2726 - loss: 1.9820
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2727 - loss: 1.9819 - val_accuracy: 0.3053 - val_loss: 1.8800
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7491
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2997 - loss: 1.8939  
[1m 70/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2925 - loss: 1.9066
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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7338
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2812 - loss: 1.8920  
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Epoch 16/125

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Epoch 17/125

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Epoch 18/125

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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.6686
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[1m 83/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3263 - loss: 1.8364
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.6100
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3171 - loss: 1.8068  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3221 - loss: 1.8210
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7046
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.7694  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3190 - loss: 1.8345 - val_accuracy: 0.3371 - val_loss: 1.7799
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7315
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3207 - loss: 1.8377  
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[1m582/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3230 - loss: 1.8227
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 1.7551
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2974 - loss: 1.7964  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3101 - loss: 1.7904
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[1m511/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3239 - loss: 1.8034
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3246 - loss: 1.8034 - val_accuracy: 0.3556 - val_loss: 1.7684
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.8748
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.8059  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.8031
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Epoch 25/125

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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8362
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3142 - loss: 1.8039  
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5000 - loss: 1.4957
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3655 - loss: 1.7335  
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.7861
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3667 - loss: 1.7145  
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 26ms/step - accuracy: 0.2500 - loss: 1.8805
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Epoch 31/125

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Epoch 32/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7013  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7024
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.5987
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3629 - loss: 1.6701  
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Epoch 34/125

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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1241
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7918  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5000 - loss: 1.4747
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[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 946us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 21: 35.15 [%]
F1-score capturado en la ejecución 21: 33.85 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 759us/step
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 810us/step
[1m137/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 740us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.15 [%]
Global F1 score (validation) = 35.89 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.3049927e-01 1.4827064e-01 1.6392983e-01 ... 9.0268280e-05
  3.5090169e-01 1.9586783e-02]
 [1.5135124e-01 1.8458498e-01 1.8048784e-01 ... 1.5322499e-04
  2.0393324e-01 1.6377773e-02]
 [7.4166551e-02 9.1697000e-02 7.9922765e-02 ... 3.6582677e-03
  8.2636327e-02 2.0748941e-02]
 ...
 [1.4420025e-01 1.5221764e-01 2.0255674e-01 ... 1.1348615e-05
  3.6003834e-01 1.2868322e-02]
 [1.0528787e-01 1.3406055e-01 1.2698220e-01 ... 2.8242695e-03
  3.1347677e-01 4.5288078e-02]
 [1.3470356e-01 1.8929660e-01 1.8337940e-01 ... 1.7959408e-04
  3.1527457e-01 1.4513720e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 46.25 [%]
Global accuracy score (test) = 35.11 [%]
Global F1 score (train) = 44.79 [%]
Global F1 score (test) = 33.36 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.21      0.22       184
       CAMINAR USUAL SPEED       0.22      0.39      0.28       184
            CAMINAR ZIGZAG       0.37      0.08      0.13       184
          DE PIE BARRIENDO       0.20      0.29      0.24       184
   DE PIE DOBLANDO TOALLAS       0.46      0.29      0.36       184
    DE PIE MOVIENDO LIBROS       0.32      0.23      0.26       184
          DE PIE USANDO PC       0.34      0.60      0.44       184
        FASE REPOSO CON K5       0.36      0.86      0.51       184
INCREMENTAL CICLOERGOMETRO       0.78      0.35      0.48       184
           SENTADO LEYENDO       0.40      0.27      0.32       184
         SENTADO USANDO PC       0.31      0.21      0.25       184
      SENTADO VIENDO LA TV       0.35      0.38      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.34      0.53      0.41       184
                    TROTAR       0.92      0.61      0.73       161

                  accuracy                           0.35      2737
                 macro avg       0.37      0.35      0.33      2737
              weighted avg       0.37      0.35      0.33      2737

2025-11-05 20:51:19.105692: 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`.
2025-11-05 20:51:19.117015: 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 00:00:1762372279.130286  199399 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372279.134518  199399 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372279.144565  199399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372279.144584  199399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372279.144585  199399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372279.144586  199399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:51:19.147707: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372281.429083  199399 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372283.120638  199509 service.cc:152] XLA service 0x721a70002400 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372283.120666  199509 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:51:23.154788: 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 00:00:1762372283.337152  199509 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372285.739821  199509 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 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4331
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1770 - loss: 2.3461  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1793 - loss: 2.3340
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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0625 - loss: 2.2479
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1946 - loss: 2.2450  
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Epoch 7/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2291 - loss: 2.1561 - val_accuracy: 0.2696 - val_loss: 2.0126
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1752
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2550 - loss: 2.1111  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2459 - loss: 2.1180
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Epoch 9/125

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[1m113/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2224 - loss: 2.1136
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[1m190/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2269 - loss: 2.1035
[1m229/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2288 - loss: 2.1007
[1m270/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.0976
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Epoch 10/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2552 - loss: 2.0270 - val_accuracy: 0.2951 - val_loss: 1.8843
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8842
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.9872  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2724 - loss: 1.9902
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[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2651 - loss: 1.9983
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[1m571/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2654 - loss: 1.9979
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Epoch 12/125

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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 1.8416
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2909 - loss: 1.8804  
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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6950
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3164 - loss: 1.9193  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2972 - loss: 1.9212 - val_accuracy: 0.3397 - val_loss: 1.7919
Epoch 15/125

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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.8813
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.8929  
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Epoch 17/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 2.2470
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2820 - loss: 1.9287  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 1.9050
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Epoch 18/125

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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.6668
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3100 - loss: 1.8365  
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[1m541/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.8523
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9548
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3054 - loss: 1.8581  
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Epoch 21/125

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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1875 - loss: 2.4365
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3131 - loss: 1.9186  
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8460
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.8107  
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.9204
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8087
[1m 31/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3486 - loss: 1.7790  
[1m 67/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.7810
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 1.9733
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7226  
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[1m192/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7283
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[1m312/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.7412
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3476 - loss: 1.7501 - val_accuracy: 0.3732 - val_loss: 1.7225
Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1591
[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3311 - loss: 1.8262  
[1m 70/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3399 - loss: 1.7938
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3515 - loss: 1.7508 - val_accuracy: 0.3782 - val_loss: 1.6838
Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 26ms/step - accuracy: 0.2812 - loss: 1.8572
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3435 - loss: 1.7600  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7499
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[1m216/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.7361
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[1m290/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3618 - loss: 1.7364
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[1m365/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.7374
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[1m551/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3611 - loss: 1.7377
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3609 - loss: 1.7378 - val_accuracy: 0.3663 - val_loss: 1.7082
Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5312 - loss: 1.6922
[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3922 - loss: 1.7503  
[1m 70/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3788 - loss: 1.7428
[1m108/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3758 - loss: 1.7325
[1m148/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3725 - loss: 1.7296
[1m187/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.7279
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.2500 - loss: 1.9541
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3893 - loss: 1.7482  
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[1m231/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3745 - loss: 1.7075
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[1m310/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3707 - loss: 1.7103
[1m347/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3696 - loss: 1.7108
[1m387/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3687 - loss: 1.7112
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[1m583/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3656 - loss: 1.7146
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3655 - loss: 1.7146 - val_accuracy: 0.3739 - val_loss: 1.6994

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 550ms/step
[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 762us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 22: 35.11 [%]
F1-score capturado en la ejecución 22: 33.36 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 65/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 784us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 756us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.39 [%]
Global F1 score (validation) = 35.06 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.16087027e-01 1.31868556e-01 1.43389866e-01 ... 7.31805903e-06
  4.57644492e-01 8.75537191e-03]
 [1.66594371e-01 1.98160768e-01 2.25565165e-01 ... 2.02199999e-06
  1.83404341e-01 6.37069112e-03]
 [1.61840498e-01 1.88628733e-01 1.74229354e-01 ... 7.20001350e-04
  1.45945892e-01 3.45867686e-02]
 ...
 [1.27383530e-01 2.03855053e-01 1.81467772e-01 ... 1.47467381e-06
  3.30213368e-01 6.99068839e-03]
 [1.12017296e-01 1.56626493e-01 1.44428462e-01 ... 5.43170492e-04
  4.07263130e-01 2.18186602e-02]
 [1.13577105e-01 1.69711173e-01 1.37246177e-01 ... 7.06700783e-04
  3.51909041e-01 2.62266733e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.91 [%]
Global accuracy score (test) = 32.88 [%]
Global F1 score (train) = 40.92 [%]
Global F1 score (test) = 31.31 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.31      0.29       184
       CAMINAR USUAL SPEED       0.36      0.04      0.08       184
            CAMINAR ZIGZAG       0.21      0.33      0.26       184
          DE PIE BARRIENDO       0.19      0.21      0.20       184
   DE PIE DOBLANDO TOALLAS       0.36      0.42      0.39       184
    DE PIE MOVIENDO LIBROS       0.38      0.31      0.34       184
          DE PIE USANDO PC       0.43      0.43      0.43       184
        FASE REPOSO CON K5       0.29      0.80      0.43       184
INCREMENTAL CICLOERGOMETRO       0.90      0.38      0.53       184
           SENTADO LEYENDO       0.13      0.11      0.12       184
         SENTADO USANDO PC       0.20      0.27      0.23       184
      SENTADO VIENDO LA TV       0.32      0.14      0.20       184
   SUBIR Y BAJAR ESCALERAS       0.37      0.60      0.46       184
                    TROTAR       0.95      0.62      0.75       161

                  accuracy                           0.33      2737
                 macro avg       0.36      0.33      0.31      2737
              weighted avg       0.35      0.33      0.31      2737

2025-11-05 20:52:13.388265: 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`.
2025-11-05 20:52:13.399506: 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 00:00:1762372333.412666  203435 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372333.416749  203435 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372333.426516  203435 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372333.426532  203435 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372333.426533  203435 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372333.426534  203435 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:52:13.429646: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372335.728171  203435 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372337.435504  203566 service.cc:152] XLA service 0x7e5e74013350 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372337.435530  203566 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:52:17.468930: 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 00:00:1762372337.639252  203566 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372340.067001  203566 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:21[0m 4s/step - accuracy: 0.0625 - loss: 4.1841
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0969 - loss: 4.3148  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0902 - loss: 4.2717
[1m115/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0887 - loss: 4.2168
[1m157/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0879 - loss: 4.1555
[1m197/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0881 - loss: 4.0929
[1m235/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0883 - loss: 4.0363
[1m274/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0885 - loss: 3.9804
[1m314/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0891 - loss: 3.9259
[1m353/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0898 - loss: 3.8761
[1m394/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0907 - loss: 3.8270
[1m435/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0917 - loss: 3.7812
[1m478/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0928 - loss: 3.7365
[1m519/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0938 - loss: 3.6970
[1m559/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.0947 - loss: 3.6611
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5280
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1519 - loss: 2.4907  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1602 - loss: 2.4747
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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.1250 - loss: 2.4637
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Epoch 6/125

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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.2987
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1985 - loss: 2.1726  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2060 - loss: 2.1698
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Epoch 8/125

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[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.0695  
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Epoch 9/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2480 - loss: 2.0648 - val_accuracy: 0.2855 - val_loss: 1.9601
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9847
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 2.0534  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0506
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Epoch 11/125

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[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2485 - loss: 2.0831  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.0510
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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0611
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0198  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2630 - loss: 2.0067
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2674 - loss: 1.9778 - val_accuracy: 0.3055 - val_loss: 1.8733
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.0397
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 1.9381  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2860 - loss: 1.9382
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[1m273/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2825 - loss: 1.9438
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Epoch 14/125

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Epoch 15/125

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Epoch 16/125

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[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3102 - loss: 1.9124  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3115 - loss: 1.9061
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Epoch 17/125

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Epoch 18/125

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Epoch 19/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.8876  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.8762
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Epoch 20/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.8174  
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.7729
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3328 - loss: 1.8094 - val_accuracy: 0.3549 - val_loss: 1.7452
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 1.8267
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2959 - loss: 1.8532  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3098 - loss: 1.8279
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8405
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7924  
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9351
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3775 - loss: 1.7795  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3636 - loss: 1.7761
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3466 - loss: 1.7759 - val_accuracy: 0.3586 - val_loss: 1.7073
Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.4645
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3681 - loss: 1.6755  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3580 - loss: 1.7042
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[1m231/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7430
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[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7540
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Epoch 26/125

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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.5000 - loss: 1.8241
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3828 - loss: 1.7058  
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.2812 - loss: 1.7583
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.7837  
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Epoch 29/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.7156  
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.5122
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3718 - loss: 1.6786  
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3590 - loss: 1.7292
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8293
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3824 - loss: 1.6570  
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Epoch 32/125

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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8429
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3345 - loss: 1.7111  
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 1.9657
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Epoch 35/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.7221  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4113
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.7026  
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.9261
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.7153  
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Epoch 38/125

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Saved model to disk.

Accuracy capturado en la ejecución 23: 32.88 [%]
F1-score capturado en la ejecución 23: 31.31 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m131/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 778us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.8 [%]
Global F1 score (validation) = 37.69 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.2625678e-01 8.7928824e-02 1.4302689e-01 ... 2.6058817e-07
  4.9845317e-01 1.7556766e-02]
 [1.1911111e-01 1.7584443e-01 1.6313894e-01 ... 4.7385074e-06
  4.1508329e-01 8.9034634e-03]
 [1.2877357e-01 1.5337613e-01 1.3969024e-01 ... 2.0583891e-03
  2.1676424e-01 5.2897427e-02]
 ...
 [1.4135134e-01 1.8232390e-01 1.7525975e-01 ... 2.6514983e-05
  3.6021918e-01 2.0715188e-02]
 [1.4753303e-01 1.9060795e-01 1.7546052e-01 ... 3.7045393e-04
  3.2519698e-01 1.6499048e-02]
 [1.2030562e-01 2.1496259e-01 1.5950553e-01 ... 6.7315949e-04
  2.4160440e-01 1.1287171e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 48.57 [%]
Global accuracy score (test) = 33.76 [%]
Global F1 score (train) = 48.51 [%]
Global F1 score (test) = 33.61 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.10      0.15       184
 CAMINAR CON MÓVIL O LIBRO       0.20      0.38      0.26       184
       CAMINAR USUAL SPEED       0.26      0.19      0.22       184
            CAMINAR ZIGZAG       0.20      0.08      0.11       184
          DE PIE BARRIENDO       0.19      0.25      0.21       184
   DE PIE DOBLANDO TOALLAS       0.35      0.39      0.37       184
    DE PIE MOVIENDO LIBROS       0.50      0.23      0.31       184
          DE PIE USANDO PC       0.36      0.60      0.45       184
        FASE REPOSO CON K5       0.39      0.62      0.48       184
INCREMENTAL CICLOERGOMETRO       0.96      0.38      0.54       184
           SENTADO LEYENDO       0.28      0.25      0.27       184
         SENTADO USANDO PC       0.20      0.20      0.20       184
      SENTADO VIENDO LA TV       0.26      0.30      0.28       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.47      0.43       184
                    TROTAR       0.91      0.65      0.76       161

                  accuracy                           0.34      2737
                 macro avg       0.38      0.34      0.34      2737
              weighted avg       0.38      0.34      0.33      2737

2025-11-05 20:53:15.254319: 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`.
2025-11-05 20:53:15.265624: 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 00:00:1762372395.278842  208265 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372395.282853  208265 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372395.292698  208265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372395.292714  208265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372395.292715  208265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372395.292717  208265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:53:15.295820: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372397.559867  208265 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372399.184580  208404 service.cc:152] XLA service 0x7b93980053d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372399.184632  208404 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:53:19.223328: 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 00:00:1762372399.394692  208404 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372401.879820  208404 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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[1m302/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0900 - loss: 3.9164
[1m343/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0912 - loss: 3.8639
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[1m506/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0944 - loss: 3.6924
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0312 - loss: 2.6946
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1484 - loss: 2.4897  
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Epoch 5/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4930
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Epoch 6/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2063 - loss: 2.2008  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2111 - loss: 2.1962 - val_accuracy: 0.2363 - val_loss: 2.0828
Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9651
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2218 - loss: 2.1015  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2205 - loss: 2.1210
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[1m229/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2204 - loss: 2.1355
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[1m306/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2202 - loss: 2.1391
[1m343/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2204 - loss: 2.1396
[1m380/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2207 - loss: 2.1398
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[1m494/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2217 - loss: 2.1390
[1m531/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.1385
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.0046
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2429 - loss: 2.0694  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2427 - loss: 2.0752
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2387 - loss: 2.0850 - val_accuracy: 0.2542 - val_loss: 1.9887
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5319
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2148 - loss: 2.1057  
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2451 - loss: 2.0569
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[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2478 - loss: 2.0516
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2479 - loss: 2.0516 - val_accuracy: 0.2995 - val_loss: 1.9186
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.0210
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2384 - loss: 2.0733  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.0620
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[1m190/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.0419
[1m230/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2526 - loss: 2.0385
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[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2535 - loss: 2.0357
[1m344/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2540 - loss: 2.0345
[1m381/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0331
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2580 - loss: 2.0259 - val_accuracy: 0.2951 - val_loss: 1.9221
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2063
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.0198  
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Epoch 12/125

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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.7784
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3015 - loss: 1.8743  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2956 - loss: 1.9130
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Epoch 14/125

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Epoch 15/125

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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7676
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.8010  
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Epoch 17/125

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Epoch 18/125

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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1605
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2946 - loss: 1.8960  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3193 - loss: 1.8510 - val_accuracy: 0.3243 - val_loss: 1.7809
Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4375 - loss: 1.5904
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7836  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.8005
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3337 - loss: 1.8219 - val_accuracy: 0.3715 - val_loss: 1.7479
Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 1.9016
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3072 - loss: 1.8485  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3178 - loss: 1.8334
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.8155
[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.8140
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[1m535/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.8109
[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3307 - loss: 1.8106
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3307 - loss: 1.8105 - val_accuracy: 0.3774 - val_loss: 1.7260
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4062 - loss: 1.9045
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.7863  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7841
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Epoch 23/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3777 - loss: 1.6983  
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.5515
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.7595  
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.6276
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.7118  
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.6079
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.9550
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3650 - loss: 1.7117  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3595 - loss: 1.7293
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 1.8959
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3563 - loss: 1.7285  
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Epoch 29/125

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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.4688 - loss: 1.7523
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3838 - loss: 1.6445  
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Epoch 31/125

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Epoch 32/125

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Epoch 33/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.6916  
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Epoch 34/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3683 - loss: 1.6762  
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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7173
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7243  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.6773
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3989 - loss: 1.6576  
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Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5312 - loss: 1.5768
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4241 - loss: 1.6473  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4130 - loss: 1.6531
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4688 - loss: 1.3851
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.6741  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3627 - loss: 1.6709
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[1m582/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3771 - loss: 1.6686
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Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0084
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3846 - loss: 1.7186  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3830 - loss: 1.6973
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Epoch 40/125

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Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5000 - loss: 1.6100
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Epoch 42/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5625 - loss: 1.5531
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4233 - loss: 1.6579  
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Epoch 43/125

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Epoch 44/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.9202
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4118 - loss: 1.6033  
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Epoch 45/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.3438 - loss: 1.8570
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3739 - loss: 1.6891  
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Epoch 46/125

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Saved model to disk.

Accuracy capturado en la ejecución 24: 33.76 [%]
F1-score capturado en la ejecución 24: 33.61 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m131/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 776us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.89 [%]
Global F1 score (validation) = 36.55 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[4.25006598e-02 4.27575149e-02 8.24482813e-02 ... 1.14531296e-07
  7.79840112e-01 1.09115029e-02]
 [1.87321067e-01 2.86116362e-01 3.07306886e-01 ... 1.30003443e-07
  6.43473938e-02 9.65180516e-04]
 [1.73909441e-01 4.00845915e-01 2.55153775e-01 ... 3.88503668e-06
  1.85901932e-02 8.97049322e-04]
 ...
 [1.63666993e-01 1.91525206e-01 2.61080444e-01 ... 2.39703672e-06
  2.48375759e-01 5.64396661e-03]
 [1.33632109e-01 1.15521155e-01 1.38198152e-01 ... 3.63699277e-03
  3.00232261e-01 4.61621098e-02]
 [9.92727727e-02 1.37824848e-01 1.53461203e-01 ... 1.76807589e-04
  4.20398504e-01 1.61570422e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.63 [%]
Global accuracy score (test) = 36.9 [%]
Global F1 score (train) = 49.4 [%]
Global F1 score (test) = 35.3 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.36      0.20      0.25       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.45      0.34       184
       CAMINAR USUAL SPEED       0.23      0.23      0.23       184
            CAMINAR ZIGZAG       0.00      0.00      0.00       184
          DE PIE BARRIENDO       0.23      0.29      0.26       184
   DE PIE DOBLANDO TOALLAS       0.35      0.32      0.33       184
    DE PIE MOVIENDO LIBROS       0.27      0.17      0.21       184
          DE PIE USANDO PC       0.34      0.65      0.44       184
        FASE REPOSO CON K5       0.40      0.75      0.52       184
INCREMENTAL CICLOERGOMETRO       0.85      0.51      0.64       184
           SENTADO LEYENDO       0.35      0.49      0.41       184
         SENTADO USANDO PC       0.23      0.17      0.20       184
      SENTADO VIENDO LA TV       0.35      0.14      0.20       184
   SUBIR Y BAJAR ESCALERAS       0.46      0.56      0.50       184
                    TROTAR       0.94      0.65      0.76       161

                  accuracy                           0.37      2737
                 macro avg       0.38      0.37      0.35      2737
              weighted avg       0.37      0.37      0.35      2737

2025-11-05 20:54:25.850053: 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`.
2025-11-05 20:54:25.861456: 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 00:00:1762372465.874640  213931 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372465.878769  213931 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372465.888796  213931 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372465.888816  213931 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372465.888817  213931 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372465.888818  213931 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:54:25.892001: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372468.147096  213931 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372469.790837  214062 service.cc:152] XLA service 0x7af4a4005890 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372469.790890  214062 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:54:29.829902: 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 00:00:1762372470.018663  214062 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372472.469482  214062 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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[1m348/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0914 - loss: 3.9092
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 26ms/step - accuracy: 0.1875 - loss: 2.4939
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Epoch 5/125

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Epoch 6/125

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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8463
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2224 - loss: 2.1064  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2233 - loss: 2.1256
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.0889
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1969 - loss: 2.1741  
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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1554
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2466 - loss: 2.0622 - val_accuracy: 0.2910 - val_loss: 1.9357
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.8407
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2863 - loss: 2.0094  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2742 - loss: 2.0086
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[1m235/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2644 - loss: 2.0129
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[1m316/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2632 - loss: 2.0145
[1m355/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2628 - loss: 2.0150
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[1m580/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2624 - loss: 2.0158
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.7933
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2786 - loss: 2.0049  
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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1458
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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3750 - loss: 1.8078
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2928 - loss: 1.9432  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2889 - loss: 1.9509
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Epoch 14/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1763
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Epoch 15/125

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Epoch 16/125

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[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3092 - loss: 1.8750  
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Epoch 17/125

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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 26ms/step - accuracy: 0.3438 - loss: 1.7611
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.0814
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2960 - loss: 1.8679  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.8500
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8990
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 2.0343
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3285 - loss: 1.8279
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 31ms/step - accuracy: 0.4062 - loss: 1.5716
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3360 - loss: 1.7783  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.7832
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7777
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7266  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7459
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3377 - loss: 1.7822 - val_accuracy: 0.3682 - val_loss: 1.7097
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7168
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3224 - loss: 1.7495  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3269 - loss: 1.7462
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[1m536/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.7775
[1m574/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7778
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3412 - loss: 1.7778 - val_accuracy: 0.3761 - val_loss: 1.7099
Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.5480
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.7302  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3597 - loss: 1.7332
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[1m319/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3562 - loss: 1.7544
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1367
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6341
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8418
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.7781  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.7554
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Epoch 29/125

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[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7106  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3562 - loss: 1.7220
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 1.7598
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7515  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3628 - loss: 1.7184 - val_accuracy: 0.3924 - val_loss: 1.6695
Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.1863
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.7485  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7375
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Epoch 32/125

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Epoch 33/125

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[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4197 - loss: 1.6290  
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.6190
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.6414  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3785 - loss: 1.6904 - val_accuracy: 0.4128 - val_loss: 1.6294
Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.6251
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3747 - loss: 1.6900  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3792 - loss: 1.6850
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[1m582/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3814 - loss: 1.6866
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3814 - loss: 1.6866 - val_accuracy: 0.3867 - val_loss: 1.6454
Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.6416
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3916 - loss: 1.6395  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3928 - loss: 1.6481
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[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3915 - loss: 1.6534
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[1m537/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.6590
[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3873 - loss: 1.6600
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3871 - loss: 1.6602 - val_accuracy: 0.3920 - val_loss: 1.6672
Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4688 - loss: 1.5920
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3950 - loss: 1.7056  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3900 - loss: 1.6965
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[1m519/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3825 - loss: 1.6794
[1m559/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3822 - loss: 1.6793
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3821 - loss: 1.6794 - val_accuracy: 0.3808 - val_loss: 1.6652
Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.4941
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3979 - loss: 1.6138  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3976 - loss: 1.6349
[1m114/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3994 - loss: 1.6316
[1m148/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3992 - loss: 1.6323
[1m188/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3988 - loss: 1.6350
[1m231/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3981 - loss: 1.6374
[1m271/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3976 - loss: 1.6391
[1m309/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3970 - loss: 1.6405
[1m346/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3965 - loss: 1.6417
[1m384/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3961 - loss: 1.6427
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[1m498/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3946 - loss: 1.6460
[1m539/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3941 - loss: 1.6468
[1m577/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3937 - loss: 1.6475
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3937 - loss: 1.6477 - val_accuracy: 0.3959 - val_loss: 1.6490
Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 1.7678
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3837 - loss: 1.6039  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3872 - loss: 1.6238
[1m116/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.6362
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[1m196/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3850 - loss: 1.6463
[1m239/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3846 - loss: 1.6499
[1m277/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.6522
[1m318/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3844 - loss: 1.6536
[1m355/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.6544
[1m396/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.6553
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[1m545/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.6580
[1m580/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.6583
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3845 - loss: 1.6583 - val_accuracy: 0.3944 - val_loss: 1.6436

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 564ms/step
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 834us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 25: 36.9 [%]
F1-score capturado en la ejecución 25: 35.3 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:48[0m 1s/step
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[1m139/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 731us/step
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[1m277/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 731us/step
[1m347/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 728us/step
[1m414/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 731us/step
[1m487/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 725us/step
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 824us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 70/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 727us/step
[1m139/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 730us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.44 [%]
Global F1 score (validation) = 37.75 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[4.21511047e-02 2.31488533e-02 3.40906940e-02 ... 4.52752261e-07
  8.52338135e-01 1.19507527e-02]
 [2.39423886e-01 2.19031453e-01 2.38804191e-01 ... 5.04491453e-08
  6.06674626e-02 2.05647200e-03]
 [1.95207164e-01 3.08801621e-01 2.30756164e-01 ... 1.07320520e-05
  7.09518939e-02 5.51319309e-03]
 ...
 [1.38173297e-01 1.27481461e-01 1.61568910e-01 ... 8.98439453e-07
  4.38005686e-01 9.79643501e-03]
 [1.13336399e-01 1.74872905e-01 1.53405488e-01 ... 4.79942901e-05
  4.33503836e-01 1.35065755e-02]
 [1.15636162e-01 1.69796422e-01 1.41902596e-01 ... 1.11236324e-04
  4.17375028e-01 2.32225824e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 49.06 [%]
Global accuracy score (test) = 36.46 [%]
Global F1 score (train) = 46.55 [%]
Global F1 score (test) = 35.48 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.39      0.09      0.15       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.41      0.31       184
       CAMINAR USUAL SPEED       0.05      0.01      0.02       184
            CAMINAR ZIGZAG       0.19      0.18      0.18       184
          DE PIE BARRIENDO       0.42      0.42      0.42       184
   DE PIE DOBLANDO TOALLAS       0.35      0.32      0.33       184
    DE PIE MOVIENDO LIBROS       0.49      0.29      0.37       184
          DE PIE USANDO PC       0.50      0.59      0.54       184
        FASE REPOSO CON K5       0.27      0.75      0.40       184
INCREMENTAL CICLOERGOMETRO       0.70      0.47      0.56       184
           SENTADO LEYENDO       0.27      0.30      0.28       184
         SENTADO USANDO PC       0.21      0.22      0.21       184
      SENTADO VIENDO LA TV       0.52      0.25      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.54      0.45       184
                    TROTAR       0.88      0.65      0.75       161

                  accuracy                           0.36      2737
                 macro avg       0.39      0.37      0.35      2737
              weighted avg       0.39      0.36      0.35      2737

2025-11-05 20:55:28.808321: 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`.
2025-11-05 20:55:28.819602: 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 00:00:1762372528.833032  218884 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372528.837246  218884 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372528.847223  218884 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372528.847245  218884 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372528.847246  218884 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372528.847248  218884 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:55:28.850389: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372531.092590  218884 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372532.763789  219014 service.cc:152] XLA service 0x78cd2400c960 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372532.763815  219014 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:55:32.796649: 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 00:00:1762372532.961387  219014 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372535.391457  219014 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 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0742 - loss: 4.2367      
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0773 - loss: 4.2119
[1m116/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0799 - loss: 4.1522
[1m157/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0822 - loss: 4.0909
[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0841 - loss: 4.0259
[1m240/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0857 - loss: 3.9634
[1m279/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0872 - loss: 3.9071
[1m318/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0885 - loss: 3.8544
[1m357/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0896 - loss: 3.8056
[1m397/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0907 - loss: 3.7593
[1m437/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0917 - loss: 3.7166
[1m474/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0926 - loss: 3.6798
[1m515/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0936 - loss: 3.6418
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

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Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.3357
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[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2086 - loss: 2.2004
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.0525
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2224 - loss: 2.1606  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2254 - loss: 2.1656
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2279 - loss: 2.1497 - val_accuracy: 0.2564 - val_loss: 2.0260
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1299
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.1266  
[1m 71/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2204 - loss: 2.1217
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[1m578/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2305 - loss: 2.1063
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2306 - loss: 2.1061 - val_accuracy: 0.2720 - val_loss: 1.9566
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 2.0020
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0763  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.0649
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[1m298/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2496 - loss: 2.0496
[1m338/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2498 - loss: 2.0485
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[1m527/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.0447
[1m565/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2504 - loss: 2.0447
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2505 - loss: 2.0447 - val_accuracy: 0.3125 - val_loss: 1.9318
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.0749
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 2.1029  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2555 - loss: 2.0776
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[1m234/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2627 - loss: 2.0465
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1175
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2787 - loss: 1.9857 - val_accuracy: 0.3175 - val_loss: 1.8774
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1044
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2469 - loss: 2.0434  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2587 - loss: 2.0164
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[1m567/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2768 - loss: 1.9740
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2770 - loss: 1.9736 - val_accuracy: 0.3167 - val_loss: 1.8496
Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.9462
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3103 - loss: 1.8950  
[1m 74/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3089 - loss: 1.8961
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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.8273
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3216 - loss: 1.8571  
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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.0829
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Epoch 17/125

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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8233
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2932 - loss: 1.9018  
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.2178
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3174 - loss: 1.8391
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Epoch 20/125

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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 2.0003
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.8284  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3210 - loss: 1.8306
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8836
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.7851  
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Epoch 23/125

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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.4375 - loss: 1.7512
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.7528  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.7690
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[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3503 - loss: 1.7752
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[1m533/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7757
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Epoch 25/125

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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.8037
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7140
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4010 - loss: 1.7444  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3915 - loss: 1.7297
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4062 - loss: 1.5373
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8249
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3630 - loss: 1.7221  
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7462
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.6273
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3438 - loss: 1.5000
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3677 - loss: 1.6371  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3668 - loss: 1.6544
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Epoch 34/125

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[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 802us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 26: 36.46 [%]
F1-score capturado en la ejecución 26: 35.48 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 68/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 752us/step
[1m127/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 803us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.32 [%]
Global F1 score (validation) = 38.16 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[9.6636899e-02 6.4346932e-02 1.1392244e-01 ... 3.6816812e-06
  5.9875542e-01 1.6104506e-02]
 [2.2878379e-01 1.4567743e-01 2.2768287e-01 ... 5.1296740e-07
  1.4427960e-01 2.0533722e-02]
 [2.1212770e-01 1.8283443e-01 2.0314030e-01 ... 6.3918669e-05
  1.3826384e-01 2.5191236e-02]
 ...
 [1.2570646e-01 1.6453245e-01 1.8555492e-01 ... 6.7955483e-07
  3.9151376e-01 8.2142325e-03]
 [1.2474194e-01 1.4885080e-01 1.4189632e-01 ... 7.0913071e-03
  2.6814073e-01 3.8837675e-02]
 [1.1602625e-01 2.0427990e-01 1.8614373e-01 ... 8.9651374e-05
  3.5736099e-01 7.5036935e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.77 [%]
Global accuracy score (test) = 30.4 [%]
Global F1 score (train) = 45.94 [%]
Global F1 score (test) = 28.93 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.22      0.22       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.27      0.27       184
       CAMINAR USUAL SPEED       0.20      0.20      0.20       184
            CAMINAR ZIGZAG       0.06      0.01      0.01       184
          DE PIE BARRIENDO       0.21      0.19      0.20       184
   DE PIE DOBLANDO TOALLAS       0.32      0.31      0.32       184
    DE PIE MOVIENDO LIBROS       0.28      0.16      0.20       184
          DE PIE USANDO PC       0.32      0.58      0.41       184
        FASE REPOSO CON K5       0.30      0.75      0.43       184
INCREMENTAL CICLOERGOMETRO       0.85      0.47      0.60       184
           SENTADO LEYENDO       0.11      0.17      0.14       184
         SENTADO USANDO PC       0.11      0.05      0.07       184
      SENTADO VIENDO LA TV       0.14      0.07      0.09       184
   SUBIR Y BAJAR ESCALERAS       0.38      0.54      0.45       184
                    TROTAR       0.92      0.61      0.74       161

                  accuracy                           0.30      2737
                 macro avg       0.31      0.31      0.29      2737
              weighted avg       0.31      0.30      0.29      2737

2025-11-05 20:56:26.901883: 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`.
2025-11-05 20:56:26.913535: 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 00:00:1762372586.926882  223322 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372586.930985  223322 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372586.940929  223322 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372586.940947  223322 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372586.940949  223322 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372586.940956  223322 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:56:26.944178: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372589.190684  223322 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372590.837715  223452 service.cc:152] XLA service 0x767fa800c0e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372590.837749  223452 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:56:30.876246: 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 00:00:1762372591.047145  223452 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372593.495273  223452 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 2/125

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Epoch 3/125

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Epoch 4/125

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Epoch 5/125

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[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1669 - loss: 2.3667  
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Epoch 6/125

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Epoch 7/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2283 - loss: 2.1300 - val_accuracy: 0.2675 - val_loss: 2.0114
Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 2.3162
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2684 - loss: 2.0735  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2555 - loss: 2.0724
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[1m318/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2445 - loss: 2.0780
[1m359/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.0786
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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.9506
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2444 - loss: 2.0539  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2425 - loss: 2.0556
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[1m232/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0519
[1m273/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2419 - loss: 2.0507
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2456 - loss: 2.0432 - val_accuracy: 0.2722 - val_loss: 1.9445
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.0302
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2355 - loss: 2.0322  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2477 - loss: 2.0255
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[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2633 - loss: 2.0049
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2633 - loss: 2.0049 - val_accuracy: 0.2962 - val_loss: 1.8979
Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0740
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2572 - loss: 1.9671  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2606 - loss: 1.9608
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[1m195/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2696 - loss: 1.9650
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[1m274/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2709 - loss: 1.9703
[1m312/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2712 - loss: 1.9732
[1m350/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2715 - loss: 1.9756
[1m390/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2717 - loss: 1.9776
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[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2727 - loss: 1.9804
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2728 - loss: 1.9805 - val_accuracy: 0.3112 - val_loss: 1.8851
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9884
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2862 - loss: 1.9543  
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Epoch 13/125

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2884 - loss: 1.9388 - val_accuracy: 0.3319 - val_loss: 1.8218
Epoch 14/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 1.9471  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2734 - loss: 1.9567
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Epoch 15/125

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[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2782 - loss: 1.9211  
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Epoch 16/125

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Epoch 17/125

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[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2864 - loss: 1.8628  
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Epoch 18/125

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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.2812 - loss: 2.2044
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7433
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3254 - loss: 1.7995  
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Epoch 21/125

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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8676
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5312 - loss: 1.4228
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3854 - loss: 1.6943  
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.6822
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.8009  
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.0619
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.7981  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.7853
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7771
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3452 - loss: 1.7771 - val_accuracy: 0.3765 - val_loss: 1.6946
Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.5000 - loss: 1.4860
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3606 - loss: 1.7546  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7556
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.5658
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7350  
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 1.6219
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3691 - loss: 1.7137  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3632 - loss: 1.7242
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[1m315/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.7360
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.5468
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7288  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.7409
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3640 - loss: 1.7361
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.7444
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3693 - loss: 1.7017  
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.2560
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.8042  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7859
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[1m314/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3572 - loss: 1.7487
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9078
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3547 - loss: 1.7682  
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Epoch 33/125

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Epoch 34/125

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[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3218 - loss: 1.8004  
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Epoch 35/125

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Epoch 36/125

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Epoch 37/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3831 - loss: 1.6552  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3769 - loss: 1.6690
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[1m342/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3753 - loss: 1.6800
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Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 1.6671
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4129 - loss: 1.5818  
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3962 - loss: 1.6528
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Epoch 39/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6953
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3770 - loss: 1.6864  
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Epoch 40/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3438 - loss: 1.7643
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3953 - loss: 1.6976  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3936 - loss: 1.6759
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3884 - loss: 1.6744
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[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3860 - loss: 1.6700
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Epoch 41/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 1.9781
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3612 - loss: 1.7423  
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Epoch 42/125

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Epoch 43/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.7641
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4036 - loss: 1.6530  
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Epoch 44/125

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Epoch 45/125

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Epoch 46/125

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Epoch 47/125

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[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4395 - loss: 1.5690  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4293 - loss: 1.5835
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4173 - loss: 1.6023
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Epoch 48/125

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[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 745us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 27: 30.4 [%]
F1-score capturado en la ejecución 27: 28.93 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m 61/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 837us/step
[1m135/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 748us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 38.41 [%]
Global F1 score (validation) = 37.7 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[4.74256948e-02 4.90971319e-02 5.66672161e-02 ... 7.25678376e-07
  7.94790864e-01 9.38131846e-03]
 [1.40551478e-01 1.95176706e-01 1.90531120e-01 ... 3.17733992e-07
  3.60143244e-01 2.74725468e-03]
 [1.53787568e-01 3.07727456e-01 2.23991975e-01 ... 2.99175532e-04
  1.00479461e-01 6.84906542e-03]
 ...
 [1.27667621e-01 1.63179919e-01 1.67715684e-01 ... 2.13934454e-05
  4.17270571e-01 1.41181424e-02]
 [1.13726743e-01 1.64310858e-01 1.48690894e-01 ... 2.84610986e-04
  4.15519089e-01 1.94200240e-02]
 [1.15440585e-01 2.38354325e-01 1.79552898e-01 ... 7.82058805e-07
  3.65289837e-01 2.49536033e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.43 [%]
Global accuracy score (test) = 38.84 [%]
Global F1 score (train) = 49.43 [%]
Global F1 score (test) = 37.53 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.50      0.02      0.03       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.42      0.30       184
       CAMINAR USUAL SPEED       0.32      0.24      0.28       184
            CAMINAR ZIGZAG       0.27      0.20      0.23       184
          DE PIE BARRIENDO       0.30      0.26      0.28       184
   DE PIE DOBLANDO TOALLAS       0.42      0.38      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.14      0.19       184
          DE PIE USANDO PC       0.39      0.60      0.47       184
        FASE REPOSO CON K5       0.36      0.85      0.51       184
INCREMENTAL CICLOERGOMETRO       0.91      0.52      0.66       184
           SENTADO LEYENDO       0.37      0.38      0.38       184
         SENTADO USANDO PC       0.21      0.24      0.22       184
      SENTADO VIENDO LA TV       0.51      0.38      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.60      0.50       184
                    TROTAR       0.91      0.63      0.75       161

                  accuracy                           0.39      2737
                 macro avg       0.43      0.39      0.38      2737
              weighted avg       0.43      0.39      0.37      2737

2025-11-05 20:57:38.877140: 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`.
2025-11-05 20:57:38.888514: 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 00:00:1762372658.901614  229168 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372658.905609  229168 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372658.915531  229168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372658.915548  229168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372658.915549  229168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372658.915550  229168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:57:38.918651: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372661.203684  229168 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372662.855178  229285 service.cc:152] XLA service 0x71ab6800cb10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372662.855203  229285 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:57:42.888000: 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 00:00:1762372663.052665  229285 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372665.467316  229285 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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[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0928 - loss: 3.8597
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Epoch 2/125

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Epoch 3/125

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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1562 - loss: 2.3700
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1567 - loss: 2.4840  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1618 - loss: 2.4702
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Epoch 5/125

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Epoch 6/125

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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.5228
[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2313 - loss: 2.1742  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2272 - loss: 2.1602
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9784
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2171 - loss: 2.1045  
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[1m540/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2384 - loss: 2.0942
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2389 - loss: 2.0936 - val_accuracy: 0.2659 - val_loss: 1.9629
Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.4688 - loss: 1.7671
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[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2543 - loss: 2.0495
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2465 - loss: 2.0701 - val_accuracy: 0.2866 - val_loss: 1.9221
Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.2342
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2561 - loss: 2.0295  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2580 - loss: 2.0309
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[1m158/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2582 - loss: 2.0306
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[1m230/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2584 - loss: 2.0274
[1m267/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2585 - loss: 2.0269
[1m307/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2589 - loss: 2.0256
[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2592 - loss: 2.0246
[1m383/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0238
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.8068
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Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 26ms/step - accuracy: 0.4062 - loss: 1.7613
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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 1.8227
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3056 - loss: 1.8663  
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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3750 - loss: 1.7825
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2771 - loss: 1.9215  
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[1m581/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2882 - loss: 1.9128
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Epoch 16/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 30ms/step - accuracy: 0.3125 - loss: 1.7457
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.8057  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3137 - loss: 1.8239
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Epoch 17/125

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Epoch 18/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 1.7514
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Epoch 19/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 1.5918
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7683
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3156 - loss: 1.7888  
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Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.4375 - loss: 1.9260
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.8289  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.8035
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3355 - loss: 1.8091 - val_accuracy: 0.3552 - val_loss: 1.7350
Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4375 - loss: 1.6228
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3570 - loss: 1.7631  
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Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 1.8557
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Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 1.6871
[1m 33/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3285 - loss: 1.8376  
[1m 68/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3273 - loss: 1.8368
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.5000 - loss: 1.3376
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3671 - loss: 1.6832  
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Epoch 26/125

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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 1.8069
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3847 - loss: 1.6957  
[1m 75/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3855 - loss: 1.7003
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Epoch 28/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9410
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.7990  
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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4375 - loss: 1.5663
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7894
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.7561  
[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3538 - loss: 1.7404
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Epoch 31/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7399
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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4688 - loss: 1.5020
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8948
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3535 - loss: 1.7686  
[1m 81/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.7394
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Epoch 34/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.4375 - loss: 1.5329
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4011 - loss: 1.7021  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3761 - loss: 1.7035 - val_accuracy: 0.3880 - val_loss: 1.6673
Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4062 - loss: 1.5033
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3941 - loss: 1.6735 - val_accuracy: 0.3943 - val_loss: 1.6836
Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 1.4959
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.6870  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.6864
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[1m235/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3719 - loss: 1.6872
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[1m315/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3746 - loss: 1.6856
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 550ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 760us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 38.84 [%]
F1-score capturado en la ejecución 28: 37.53 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 796us/step
[1m130/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 778us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.65 [%]
Global F1 score (validation) = 38.1 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[7.97205791e-02 9.40576643e-02 1.06064796e-01 ... 1.86104189e-05
  5.75889587e-01 2.30929125e-02]
 [1.70291305e-01 1.60640433e-01 2.00013712e-01 ... 3.10815881e-06
  2.89243668e-01 1.00106895e-02]
 [1.91072956e-01 2.19414622e-01 2.26480693e-01 ... 7.95354135e-05
  1.28156096e-01 1.92523524e-02]
 ...
 [1.37071580e-01 1.60113826e-01 2.02745929e-01 ... 3.13559205e-08
  3.62961501e-01 7.88204372e-03]
 [1.31499767e-01 1.86218992e-01 1.92921370e-01 ... 6.07806651e-05
  3.38117987e-01 1.46472221e-02]
 [9.90143493e-02 2.66021281e-01 1.98093861e-01 ... 8.90315914e-06
  3.20214778e-01 4.19389969e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 49.12 [%]
Global accuracy score (test) = 37.16 [%]
Global F1 score (train) = 48.55 [%]
Global F1 score (test) = 36.49 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.26      0.26       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.38      0.30       184
       CAMINAR USUAL SPEED       0.23      0.13      0.17       184
            CAMINAR ZIGZAG       0.17      0.05      0.08       184
          DE PIE BARRIENDO       0.26      0.27      0.26       184
   DE PIE DOBLANDO TOALLAS       0.41      0.40      0.40       184
    DE PIE MOVIENDO LIBROS       0.43      0.29      0.35       184
          DE PIE USANDO PC       0.37      0.59      0.45       184
        FASE REPOSO CON K5       0.35      0.75      0.48       184
INCREMENTAL CICLOERGOMETRO       0.84      0.38      0.52       184
           SENTADO LEYENDO       0.38      0.36      0.37       184
         SENTADO USANDO PC       0.28      0.23      0.25       184
      SENTADO VIENDO LA TV       0.40      0.38      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.52      0.45       184
                    TROTAR       0.92      0.62      0.74       161

                  accuracy                           0.37      2737
                 macro avg       0.40      0.37      0.36      2737
              weighted avg       0.39      0.37      0.36      2737

2025-11-05 20:58:38.849984: 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`.
2025-11-05 20:58:38.861144: 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 00:00:1762372718.874138  233799 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762372718.878197  233799 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762372718.887868  233799 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372718.887884  233799 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372718.887885  233799 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762372718.887887  233799 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 20:58:38.891024: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762372721.160711  233799 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..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/125
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762372722.825149  233931 service.cc:152] XLA service 0x72ca0000bb40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762372722.825193  233931 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 20:58:42.863418: 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 00:00:1762372723.028820  233931 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762372725.452031  233931 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:15[0m 4s/step - accuracy: 0.0000e+00 - loss: 5.1603
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0697 - loss: 4.4706      
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0717 - loss: 4.3679
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[1m159/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0750 - loss: 4.2064
[1m197/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0764 - loss: 4.1415
[1m237/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0776 - loss: 4.0768
[1m275/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0785 - loss: 4.0201
[1m316/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0796 - loss: 3.9630
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Epoch 2/125

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Epoch 3/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1250 - loss: 2.5405
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[1m 76/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1380 - loss: 2.5343
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Epoch 4/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0938 - loss: 2.5342
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Epoch 5/125

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[1m 34/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1835 - loss: 2.2672  
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[1m571/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1983 - loss: 2.2614
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1984 - loss: 2.2609 - val_accuracy: 0.2259 - val_loss: 2.1060
Epoch 6/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1090
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.1759  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2263 - loss: 2.1895
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[1m302/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2176 - loss: 2.1902
[1m342/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2171 - loss: 2.1897
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Epoch 7/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 2.0263
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2220 - loss: 2.1647  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2237 - loss: 2.1677
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[1m231/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2257 - loss: 2.1608
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Epoch 8/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.0907
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.1174  
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Epoch 9/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.0536
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2596 - loss: 2.0239  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2549 - loss: 2.0363
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[1m306/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0478
[1m347/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0480
[1m383/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2539 - loss: 2.0479
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[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2544 - loss: 2.0459
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Epoch 10/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 1.8921
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2898 - loss: 2.0022  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2784 - loss: 2.0025
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Epoch 11/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 1.8987
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2609 - loss: 2.0034 - val_accuracy: 0.3162 - val_loss: 1.8601
Epoch 12/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4375 - loss: 2.0217
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3021 - loss: 1.9992  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2944 - loss: 1.9885
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[1m273/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2858 - loss: 1.9628
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[1m348/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2845 - loss: 1.9600
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Epoch 13/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.0429
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2751 - loss: 1.9619  
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Epoch 14/125

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Epoch 15/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9183
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3044 - loss: 1.9237  
[1m 77/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3070 - loss: 1.9152
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Epoch 16/125

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Epoch 17/125

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Epoch 18/125

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[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.8572  
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Epoch 19/125

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[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 1.8217  
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Epoch 20/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3750 - loss: 1.8407
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3410 - loss: 1.8069 - val_accuracy: 0.3702 - val_loss: 1.7235
Epoch 21/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7123
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.8149  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.8030
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[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7984
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Epoch 22/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.4062 - loss: 1.7123
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7730  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7770
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3431 - loss: 1.7792 - val_accuracy: 0.3826 - val_loss: 1.6902
Epoch 23/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8469
[1m 40/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3313 - loss: 1.7882  
[1m 82/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7851
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[1m579/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7806
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3460 - loss: 1.7806 - val_accuracy: 0.3641 - val_loss: 1.7263
Epoch 24/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 1.6097
[1m 36/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3303 - loss: 1.7142  
[1m 73/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.7283
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Epoch 25/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.0719
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.7427  
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Epoch 26/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 1.6301
[1m 41/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3708 - loss: 1.7349  
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Epoch 27/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 1.6220
[1m 39/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7171  
[1m 80/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7341
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Epoch 28/125

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Epoch 29/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 1.5298
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Epoch 30/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8306
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Epoch 31/125

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Epoch 32/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8219
[1m 35/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.7581  
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Epoch 33/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 1.9212
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3861 - loss: 1.6598  
[1m 78/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3835 - loss: 1.6607
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Epoch 34/125

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Epoch 35/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1690
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7840  
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Epoch 36/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3750 - loss: 1.5457
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4347 - loss: 1.5951  
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[1m538/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3968 - loss: 1.6504
[1m576/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3959 - loss: 1.6519
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3957 - loss: 1.6522 - val_accuracy: 0.3946 - val_loss: 1.6578
Epoch 37/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.4062 - loss: 1.7417
[1m 37/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4051 - loss: 1.6692  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3924 - loss: 1.6723
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[1m154/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3921 - loss: 1.6635
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[1m237/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3923 - loss: 1.6619
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[1m318/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3917 - loss: 1.6618
[1m357/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3915 - loss: 1.6619
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3911 - loss: 1.6626 - val_accuracy: 0.3854 - val_loss: 1.6441
Epoch 38/125

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7069
[1m 38/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3700 - loss: 1.7213  
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3789 - loss: 1.6909
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[1m158/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3876 - loss: 1.6734
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[1m237/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3891 - loss: 1.6660
[1m277/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3894 - loss: 1.6642
[1m316/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3896 - loss: 1.6632
[1m355/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3898 - loss: 1.6621
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[1m436/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3906 - loss: 1.6608
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[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 729us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 37.16 [%]
F1-score capturado en la ejecución 29: 36.49 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 244,623 (955.56 KB)
 Trainable params: 244,623 (955.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18676, 3, 250)

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[1m130/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 784us/step
[1m202/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 757us/step
[1m272/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 746us/step
[1m332/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 762us/step
[1m402/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 755us/step
[1m475/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 745us/step
[1m542/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 745us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 739us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 815us/step
[1m130/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 781us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.43 [%]
Global F1 score (validation) = 38.31 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[8.4749714e-02 8.0864266e-02 1.1119685e-01 ... 1.4686867e-06
  6.4378238e-01 6.3511864e-03]
 [1.9674705e-01 2.4450225e-01 2.3022151e-01 ... 8.0045384e-06
  1.4250946e-01 4.4123260e-03]
 [1.4656973e-01 1.7400354e-01 1.4836629e-01 ... 7.4312935e-04
  6.3407496e-02 1.6704826e-02]
 ...
 [1.5849170e-01 1.2286382e-01 1.8570471e-01 ... 8.2163824e-06
  3.4581414e-01 1.6974330e-02]
 [1.2269791e-01 1.5252198e-01 1.4090598e-01 ... 2.0506470e-03
  2.9394272e-01 2.6642233e-02]
 [8.3540186e-02 1.3123566e-01 1.2739599e-01 ... 3.0546951e-05
  5.4313934e-01 1.6205097e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 48.05 [%]
Global accuracy score (test) = 33.1 [%]
Global F1 score (train) = 47.69 [%]
Global F1 score (test) = 32.5 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.17      0.20       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.26      0.24       184
       CAMINAR USUAL SPEED       0.25      0.16      0.19       184
            CAMINAR ZIGZAG       0.07      0.03      0.04       184
          DE PIE BARRIENDO       0.30      0.49      0.37       184
   DE PIE DOBLANDO TOALLAS       0.41      0.28      0.33       184
    DE PIE MOVIENDO LIBROS       0.31      0.29      0.30       184
          DE PIE USANDO PC       0.38      0.46      0.42       184
        FASE REPOSO CON K5       0.26      0.76      0.39       184
INCREMENTAL CICLOERGOMETRO       0.86      0.34      0.49       184
           SENTADO LEYENDO       0.17      0.11      0.14       184
         SENTADO USANDO PC       0.20      0.17      0.18       184
      SENTADO VIENDO LA TV       0.47      0.38      0.42       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.51      0.44       184
                    TROTAR       0.93      0.60      0.73       161

                  accuracy                           0.33      2737
                 macro avg       0.36      0.33      0.33      2737
              weighted avg       0.36      0.33      0.32      2737


Accuracy capturado en la ejecución 30: 33.1 [%]
F1-score capturado en la ejecución 30: 32.5 [%]

=== RESUMEN FINAL ===
Accuracies: [36.83, 35.92, 35.84, 33.76, 36.68, 34.97, 35.7, 34.6, 35.73, 34.2, 36.5, 36.57, 35.55, 34.86, 37.08, 36.35, 35.04, 32.81, 34.93, 34.45, 35.15, 35.11, 32.88, 33.76, 36.9, 36.46, 30.4, 38.84, 37.16, 33.1]
F1-scores: [34.36, 33.32, 34.76, 32.67, 35.77, 33.99, 34.3, 32.47, 34.92, 32.5, 34.55, 35.23, 33.15, 33.54, 35.39, 34.96, 33.46, 31.34, 32.88, 33.24, 33.85, 33.36, 31.31, 33.61, 35.3, 35.48, 28.93, 37.53, 36.49, 32.5]
Accuracy mean: 35.2710 | std: 1.6448
F1 mean: 33.8387 | std: 1.6727

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_ESANN_acc_17_classes/metrics_test.npz
