2025-11-04 12:32:33.204407: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:32:33.216247: 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 00:00:1762255953.230043 1299904 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762255953.234326 1299904 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762255953.244738 1299904 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762255953.244759 1299904 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762255953.244762 1299904 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762255953.244763 1299904 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:32:33.247989: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-04 12:32:37,366	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-04 12:32:38,061	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-04 12:32:38,141	INFO trial.py:182 -- Creating a new dirname dir_f7582_fc1b because trial dirname 'dir_f7582' already exists.
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2025-11-04 12:32:38,164	INFO trial.py:182 -- Creating a new dirname dir_f7582_fa09 because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,169	INFO trial.py:182 -- Creating a new dirname dir_f7582_a545 because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,172	INFO trial.py:182 -- Creating a new dirname dir_f7582_364c because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,175	INFO trial.py:182 -- Creating a new dirname dir_f7582_cbb5 because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,178	INFO trial.py:182 -- Creating a new dirname dir_f7582_c3be because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,182	INFO trial.py:182 -- Creating a new dirname dir_f7582_0a66 because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,185	INFO trial.py:182 -- Creating a new dirname dir_f7582_6e1a because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,192	INFO trial.py:182 -- Creating a new dirname dir_f7582_d3bb because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,198	INFO trial.py:182 -- Creating a new dirname dir_f7582_6aa7 because trial dirname 'dir_f7582' already exists.
2025-11-04 12:32:38,214	INFO trial.py:182 -- Creating a new dirname dir_f7582_0078 because trial dirname 'dir_f7582' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     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_gyr_superclasses_CPA_METs/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-04_12-32-35_648040_1299904/artifacts/2025-11-04_12-32-38/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-04 12:32:38. Total running time: 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 │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_f7582    PENDING            4   adam            relu                                   32                 64                  5          0.000844071         78 │
│ trial_f7582    PENDING            2   rmsprop         tanh                                  128                 64                  3          0.00129301          89 │
│ trial_f7582    PENDING            4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105 │
│ trial_f7582    PENDING            2   rmsprop         tanh                                  128                 32                  5          0.00124114         128 │
│ trial_f7582    PENDING            2   adam            tanh                                   32                 32                  5          3.84427e-05        126 │
│ trial_f7582    PENDING            4   adam            tanh                                  128                 64                  5          0.000409061        122 │
│ trial_f7582    PENDING            2   rmsprop         relu                                   32                 64                  5          0.00254553          76 │
│ trial_f7582    PENDING            3   rmsprop         relu                                  128                 32                  5          0.00057906         134 │
│ trial_f7582    PENDING            3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104 │
│ trial_f7582    PENDING            4   adam            relu                                   32                128                  3          0.000172281         84 │
│ trial_f7582    PENDING            2   adam            relu                                  128                 64                  3          1.12827e-05         79 │
│ trial_f7582    PENDING            2   rmsprop         tanh                                  128                 32                  3          0.00148034          65 │
│ trial_f7582    PENDING            2   adam            relu                                   64                128                  3          1.11943e-05         89 │
│ trial_f7582    PENDING            2   adam            tanh                                   32                 64                  5          2.65327e-05        102 │
│ trial_f7582    PENDING            2   adam            relu                                   64                 64                  3          0.0011964          137 │
│ trial_f7582    PENDING            2   adam            tanh                                   64                 64                  3          6.55492e-05        122 │
│ trial_f7582    PENDING            4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92 │
│ trial_f7582    PENDING            2   adam            tanh                                  128                 32                  3          8.15724e-05         95 │
│ trial_f7582    PENDING            2   rmsprop         tanh                                   32                128                  3          0.00153955          78 │
│ trial_f7582    PENDING            3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            89 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00129 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           134 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00058 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            78 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00084 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            92 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00006 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           126 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           122 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            65 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00148 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           105 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            84 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00017 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            82 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           104 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00008 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            79 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           122 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00041 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
[36m(train_cnn_ray_tune pid=1301518)[0m 2025-11-04 12:32:41.235754: 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=1301518)[0m 2025-11-04 12:32:41.257182: 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=1301518)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=1301518)[0m E0000 00:00:1762255961.285646 1302684 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=1301518)[0m E0000 00:00:1762255961.293162 1302684 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=1301518)[0m W0000 00:00:1762255961.312454 1302684 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=1301518)[0m W0000 00:00:1762255961.312508 1302684 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=1301518)[0m W0000 00:00:1762255961.312511 1302684 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=1301518)[0m W0000 00:00:1762255961.312513 1302684 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=1301518)[0m 2025-11-04 12:32:41.318464: 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=1301518)[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=1301518)[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=1301518)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=1301518)[0m 2025-11-04 12:32:44.493523: 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=1301518)[0m 2025-11-04 12:32:44.493568: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=1301518)[0m 2025-11-04 12:32:44.493575: 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=1301518)[0m 2025-11-04 12:32:44.493581: 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=1301518)[0m 2025-11-04 12:32:44.493586: 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=1301518)[0m 2025-11-04 12:32:44.493590: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=1301518)[0m 2025-11-04 12:32:44.493804: 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=1301518)[0m 2025-11-04 12:32:44.493836: 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=1301518)[0m 2025-11-04 12:32:44.493841: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭─────────────────────────────────────╮
│ Trial trial_f7582 config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                          137 │
│ funcion_activacion             relu │
│ numero_filtros                   64 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 64 │
│ tasa_aprendizaje             0.0012 │
╰─────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           128 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00124 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           102 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            76 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00255 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            78 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00154 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            95 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00008 │
╰──────────────────────────────────────╯
Trial trial_f7582 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_f7582 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            89 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1301518)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=1301518)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=1301518)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=1301518)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=1301518)[0m │ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
[36m(train_cnn_ray_tune pid=1301518)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1301518)[0m │ layer_normalization             │ (None, 6, 64)          │           128 │
[36m(train_cnn_ray_tune pid=1301518)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=1301518)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1301518)[0m │ dropout (Dropout)               │ (None, 6, 64)          │             0 │
[36m(train_cnn_ray_tune pid=1301518)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1301518)[0m │ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
[36m(train_cnn_ray_tune pid=1301518)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1301518)[0m │ layer_normalization_1           │ (None, 6, 64)          │           128 │
[36m(train_cnn_ray_tune pid=1301518)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=1301518)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1301518)[0m │ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
[36m(train_cnn_ray_tune pid=1301518)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1301518)[0m │ global_average_pooling1d        │ (None, 64)             │             0 │
[36m(train_cnn_ray_tune pid=1301518)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=1301518)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1301518)[0m │ dropout_2 (Dropout)             │ (None, 64)             │             0 │
[36m(train_cnn_ray_tune pid=1301518)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1301518)[0m │ dense (Dense)                   │ (None, 4)              │           260 │
[36m(train_cnn_ray_tune pid=1301518)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=1301518)[0m  Total params: 60,932 (238.02 KB)
[36m(train_cnn_ray_tune pid=1301518)[0m  Trainable params: 60,932 (238.02 KB)
[36m(train_cnn_ray_tune pid=1301518)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=1301518)[0m Epoch 1/89
[36m(train_cnn_ray_tune pid=1301555)[0m  Total params: 325,508 (1.24 MB)
[36m(train_cnn_ray_tune pid=1301555)[0m  Trainable params: 325,508 (1.24 MB)
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:30[0m 3s/step - accuracy: 0.2734 - loss: 2.1799
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.2625 - loss: 2.1776
[1m 7/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.2652 - loss: 2.1525
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m10/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.2659 - loss: 2.1397
[1m13/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.2677 - loss: 2.1296
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m16/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.2691 - loss: 2.1163
[1m19/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.2699 - loss: 2.1053
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m23/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.2706 - loss: 2.0931
[1m26/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.2715 - loss: 2.0823
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m29/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.2723 - loss: 2.0719
[36m(train_cnn_ray_tune pid=1301557)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:17[0m 2s/step - accuracy: 0.2656 - loss: 2.1084
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.2705 - loss: 2.1466
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m32/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.2734 - loss: 2.0616
[1m35/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 20ms/step - accuracy: 0.2741 - loss: 2.0526
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 20ms/step - accuracy: 0.2746 - loss: 2.0442
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2870 - loss: 1.9492
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.2758 - loss: 1.9671
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m 12/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2750 - loss: 1.9583
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.2751 - loss: 1.9522
[36m(train_cnn_ray_tune pid=1301534)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.2565 - loss: 2.2034 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.2655 - loss: 2.1805
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2767 - loss: 1.9401
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.2778 - loss: 1.9331
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2834 - loss: 1.8937
[36m(train_cnn_ray_tune pid=1301551)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.1758 - loss: 2.5066 
[36m(train_cnn_ray_tune pid=1301567)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.2674 - loss: 2.0603
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.2548 - loss: 2.0541
[36m(train_cnn_ray_tune pid=1301534)[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=1301534)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m │ global_average_pooling1d        │ (None, 128)            │             0 │[32m [repeated 102x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 191x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m │ layer_normalization             │ (None, 6, 128)         │           256 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 51x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m │ dropout (Dropout)               │ (None, 6, 128)         │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m │ dropout_2 (Dropout)             │ (None, 128)            │             0 │[32m [repeated 51x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m │ dense (Dense)                   │ (None, 4)              │           516 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m  Total params: 146,436 (572.02 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m  Trainable params: 146,436 (572.02 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m Epoch 1/78[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.2915 - loss: 1.8277
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 25ms/step - accuracy: 0.2917 - loss: 1.8259
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 25ms/step - accuracy: 0.2921 - loss: 1.8224
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 40ms/step - accuracy: 0.2798 - loss: 1.9454 - val_accuracy: 0.3867 - val_loss: 1.2697
[36m(train_cnn_ray_tune pid=1301532)[0m 
[1m27/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 46ms/step - accuracy: 0.2509 - loss: 2.1248
[1m28/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step - accuracy: 0.2512 - loss: 2.1242
[1m29/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step - accuracy: 0.2515 - loss: 2.1234
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 123ms/step - accuracy: 0.3516 - loss: 1.4508[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 43ms/step - accuracy: 0.3188 - loss: 1.5427
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[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m49/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 44ms/step - accuracy: 0.3191 - loss: 1.5371[32m [repeated 120x across cluster][0m
[36m(train_cnn_ray_tune pid=1301526)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41:17[0m 7s/step - accuracy: 0.3125 - loss: 1.8031
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[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3108 - loss: 1.6829[32m [repeated 197x across cluster][0m
[36m(train_cnn_ray_tune pid=1301507)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 122ms/step - accuracy: 0.2441 - loss: 2.1443
[36m(train_cnn_ray_tune pid=1301524)[0m 
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m7s[0m 62ms/step - accuracy: 0.2565 - loss: 2.1195
[1m 47/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m7s[0m 62ms/step - accuracy: 0.2563 - loss: 2.1196[32m [repeated 160x across cluster][0m
[36m(train_cnn_ray_tune pid=1301567)[0m 
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.2470 - loss: 2.1093 
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.2472 - loss: 2.1131[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1301507)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 107ms/step - accuracy: 0.2478 - loss: 2.1252
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 98ms/step - accuracy: 0.2454 - loss: 2.1241 
[36m(train_cnn_ray_tune pid=1301552)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 82ms/step - accuracy: 0.2656 - loss: 2.2098[32m [repeated 29x across cluster][0m
[36m(train_cnn_ray_tune pid=1301574)[0m 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m12s[0m 49ms/step - accuracy: 0.2444 - loss: 2.2129
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m12s[0m 49ms/step - accuracy: 0.2445 - loss: 2.2117[32m [repeated 23x across cluster][0m
[36m(train_cnn_ray_tune pid=1301574)[0m 
[1m 82/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m12s[0m 49ms/step - accuracy: 0.2449 - loss: 2.2061
[1m 83/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m12s[0m 49ms/step - accuracy: 0.2450 - loss: 2.2055
[1m 84/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m12s[0m 49ms/step - accuracy: 0.2450 - loss: 2.2049
[36m(train_cnn_ray_tune pid=1301532)[0m Epoch 2/79[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m 
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m5s[0m 37ms/step - accuracy: 0.3005 - loss: 1.8421
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m5s[0m 37ms/step - accuracy: 0.3006 - loss: 1.8411
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m5s[0m 37ms/step - accuracy: 0.3007 - loss: 1.8402
[36m(train_cnn_ray_tune pid=1301560)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 132ms/step - accuracy: 0.3125 - loss: 2.0779
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[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 57ms/step - accuracy: 0.3108 - loss: 2.0612
[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 55ms/step - accuracy: 0.3197 - loss: 1.5204 - val_accuracy: 0.4166 - val_loss: 1.2454[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1301532)[0m 
[1m20/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 49ms/step - accuracy: 0.2737 - loss: 1.9833
[1m21/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 49ms/step - accuracy: 0.2734 - loss: 1.9846
[1m22/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 49ms/step - accuracy: 0.2732 - loss: 1.9860[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1301574)[0m 
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 50ms/step - accuracy: 0.2470 - loss: 2.1882
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m10s[0m 50ms/step - accuracy: 0.2470 - loss: 2.1877
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 33ms/step - accuracy: 0.2509 - loss: 1.9380  
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m Epoch 3/137[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-04 12:33:08. 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_f7582    RUNNING            4   adam            relu                                   32                 64                  5          0.000844071         78 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 64                  3          0.00129301          89 │
│ trial_f7582    RUNNING            4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 32                  5          0.00124114         128 │
│ trial_f7582    RUNNING            2   adam            tanh                                   32                 32                  5          3.84427e-05        126 │
│ trial_f7582    RUNNING            4   adam            tanh                                  128                 64                  5          0.000409061        122 │
│ trial_f7582    RUNNING            2   rmsprop         relu                                   32                 64                  5          0.00254553          76 │
│ trial_f7582    RUNNING            3   rmsprop         relu                                  128                 32                  5          0.00057906         134 │
│ trial_f7582    RUNNING            3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104 │
│ trial_f7582    RUNNING            4   adam            relu                                   32                128                  3          0.000172281         84 │
│ trial_f7582    RUNNING            2   adam            relu                                  128                 64                  3          1.12827e-05         79 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 32                  3          0.00148034          65 │
│ trial_f7582    RUNNING            2   adam            relu                                   64                128                  3          1.11943e-05         89 │
│ trial_f7582    RUNNING            2   adam            tanh                                   32                 64                  5          2.65327e-05        102 │
│ trial_f7582    RUNNING            2   adam            relu                                   64                 64                  3          0.0011964          137 │
│ trial_f7582    RUNNING            2   adam            tanh                                   64                 64                  3          6.55492e-05        122 │
│ trial_f7582    RUNNING            4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92 │
│ trial_f7582    RUNNING            2   adam            tanh                                  128                 32                  3          8.15724e-05         95 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00153955          78 │
│ trial_f7582    RUNNING            3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 36ms/step - accuracy: 0.3224 - loss: 1.9751
[36m(train_cnn_ray_tune pid=1301507)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 86ms/step - accuracy: 0.2578 - loss: 1.8353  
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[36m(train_cnn_ray_tune pid=1301574)[0m Epoch 2/92[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m Epoch 3/105[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 42ms/step - accuracy: 0.3633 - loss: 1.2852 - val_accuracy: 0.3965 - val_loss: 1.2096[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m Epoch 6/82[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m Epoch 9/95[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m Epoch 4/102[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 61ms/step - accuracy: 0.2623 - loss: 2.0757
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 60ms/step - accuracy: 0.2623 - loss: 2.0756[32m [repeated 183x across cluster][0m
[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 117ms/step - accuracy: 0.3047 - loss: 1.7456
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m Epoch 4/78[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 48ms/step - accuracy: 0.5312 - loss: 1.0729 - val_accuracy: 0.5782 - val_loss: 0.9266[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 108ms/step - accuracy: 0.3359 - loss: 1.6779[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-04 12:33:38. 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_f7582    RUNNING            4   adam            relu                                   32                 64                  5          0.000844071         78 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 64                  3          0.00129301          89 │
│ trial_f7582    RUNNING            4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 32                  5          0.00124114         128 │
│ trial_f7582    RUNNING            2   adam            tanh                                   32                 32                  5          3.84427e-05        126 │
│ trial_f7582    RUNNING            4   adam            tanh                                  128                 64                  5          0.000409061        122 │
│ trial_f7582    RUNNING            2   rmsprop         relu                                   32                 64                  5          0.00254553          76 │
│ trial_f7582    RUNNING            3   rmsprop         relu                                  128                 32                  5          0.00057906         134 │
│ trial_f7582    RUNNING            3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104 │
│ trial_f7582    RUNNING            4   adam            relu                                   32                128                  3          0.000172281         84 │
│ trial_f7582    RUNNING            2   adam            relu                                  128                 64                  3          1.12827e-05         79 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 32                  3          0.00148034          65 │
│ trial_f7582    RUNNING            2   adam            relu                                   64                128                  3          1.11943e-05         89 │
│ trial_f7582    RUNNING            2   adam            tanh                                   32                 64                  5          2.65327e-05        102 │
│ trial_f7582    RUNNING            2   adam            relu                                   64                 64                  3          0.0011964          137 │
│ trial_f7582    RUNNING            2   adam            tanh                                   64                 64                  3          6.55492e-05        122 │
│ trial_f7582    RUNNING            4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92 │
│ trial_f7582    RUNNING            2   adam            tanh                                  128                 32                  3          8.15724e-05         95 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00153955          78 │
│ trial_f7582    RUNNING            3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m Epoch 6/89[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m Epoch 8/137[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m Epoch 5/78[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m Epoch 7/126[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m Epoch 10/137[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34s[0m 105ms/step - accuracy: 0.2812 - loss: 1.6359
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 11/122[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-04 12:34:08. 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_f7582    RUNNING            4   adam            relu                                   32                 64                  5          0.000844071         78 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 64                  3          0.00129301          89 │
│ trial_f7582    RUNNING            4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 32                  5          0.00124114         128 │
│ trial_f7582    RUNNING            2   adam            tanh                                   32                 32                  5          3.84427e-05        126 │
│ trial_f7582    RUNNING            4   adam            tanh                                  128                 64                  5          0.000409061        122 │
│ trial_f7582    RUNNING            2   rmsprop         relu                                   32                 64                  5          0.00254553          76 │
│ trial_f7582    RUNNING            3   rmsprop         relu                                  128                 32                  5          0.00057906         134 │
│ trial_f7582    RUNNING            3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104 │
│ trial_f7582    RUNNING            4   adam            relu                                   32                128                  3          0.000172281         84 │
│ trial_f7582    RUNNING            2   adam            relu                                  128                 64                  3          1.12827e-05         79 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                  128                 32                  3          0.00148034          65 │
│ trial_f7582    RUNNING            2   adam            relu                                   64                128                  3          1.11943e-05         89 │
│ trial_f7582    RUNNING            2   adam            tanh                                   32                 64                  5          2.65327e-05        102 │
│ trial_f7582    RUNNING            2   adam            relu                                   64                 64                  3          0.0011964          137 │
│ trial_f7582    RUNNING            2   adam            tanh                                   64                 64                  3          6.55492e-05        122 │
│ trial_f7582    RUNNING            4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92 │
│ trial_f7582    RUNNING            2   adam            tanh                                  128                 32                  3          8.15724e-05         95 │
│ trial_f7582    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00153955          78 │
│ trial_f7582    RUNNING            3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m Epoch 19/89[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 420ms/step
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
[1m10/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1301524)[0m 
[1m13/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1301524)[0m 
[1m16/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[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=1301524)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301534)[0m 2025-11-04 12:32:41.922326: 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=1301534)[0m 2025-11-04 12:32:41.941755: 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=1301534)[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=1301534)[0m E0000 00:00:1762255961.979623 1302811 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=1301534)[0m E0000 00:00:1762255961.988077 1302811 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=1301534)[0m W0000 00:00:1762255962.008728 1302811 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=1301534)[0m 2025-11-04 12:32:42.014887: 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=1301534)[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=1301534)[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=1301534)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m 2025-11-04 12:32:45.207255: 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=1301534)[0m 2025-11-04 12:32:45.207351: 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=1301534)[0m 2025-11-04 12:32:45.207363: 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=1301534)[0m 2025-11-04 12:32:45.207372: 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=1301534)[0m 2025-11-04 12:32:45.207378: 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=1301534)[0m 2025-11-04 12:32:45.207383: 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=1301534)[0m 2025-11-04 12:32:45.207853: 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=1301534)[0m 2025-11-04 12:32:45.207916: 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=1301534)[0m 2025-11-04 12:32:45.207922: 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=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m Epoch 12/137[32m [repeated 14x across cluster][0m
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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[36m(train_cnn_ray_tune pid=1301524)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:34:16. Total running time: 1min 37s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             94.9735 │
│ time_total_s                 94.9735 │
│ training_iteration                 1 │
│ val_accuracy                 0.38469 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:34:16. Total running time: 1min 37s
[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m Epoch 12/122[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m Epoch 25/128[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 151ms/step - accuracy: 0.5078 - loss: 1.1210[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m Epoch 25/95[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m Epoch 24/79[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[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=1301544)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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[36m(train_cnn_ray_tune pid=1301544)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:34:38. Total running time: 1min 59s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              116.78 │
│ time_total_s                  116.78 │
│ training_iteration                 1 │
│ val_accuracy                 0.43857 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:34:38. Total running time: 1min 59s
[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[1m 8/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 19ms/step

Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-04 12:34:38. Total running time: 2min 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_f7582    RUNNING              4   adam            relu                                   32                 64                  5          0.000844071         78                                              │
│ trial_f7582    RUNNING              2   rmsprop         tanh                                  128                 64                  3          0.00129301          89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 32                  5          3.84427e-05        126                                              │
│ trial_f7582    RUNNING              4   adam            tanh                                  128                 64                  5          0.000409061        122                                              │
│ trial_f7582    RUNNING              2   rmsprop         relu                                   32                 64                  5          0.00254553          76                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.00057906         134                                              │
│ trial_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                  128                 64                  3          1.12827e-05         79                                              │
│ trial_f7582    RUNNING              2   rmsprop         tanh                                  128                 32                  3          0.00148034          65                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 64                  5          2.65327e-05        102                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                 64                  3          0.0011964          137                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    RUNNING              4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                  128                 32                  3          8.15724e-05         95                                              │
│ trial_f7582    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00153955          78                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82                                              │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[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=1301574)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m Epoch 26/89[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301574)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:34:41. Total running time: 2min 3s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             121.008 │
│ time_total_s                 121.008 │
│ training_iteration                 1 │
│ val_accuracy                 0.40407 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:34:41. Total running time: 2min 3s
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[36m(train_cnn_ray_tune pid=1301557)[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=1301557)[0m   _log_deprecation_warning(
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:34:49. Total running time: 2min 11s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             128.338 │
│ time_total_s                 128.338 │
│ training_iteration                 1 │
│ val_accuracy                 0.44875 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:34:49. Total running time: 2min 11s
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m Epoch 11/76[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301557)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m Epoch 32/95[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m Epoch 33/95[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m Epoch 33/89[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-04 12:35:08. Total running time: 2min 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_f7582    RUNNING              4   adam            relu                                   32                 64                  5          0.000844071         78                                              │
│ trial_f7582    RUNNING              2   rmsprop         tanh                                  128                 64                  3          0.00129301          89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 32                  5          3.84427e-05        126                                              │
│ trial_f7582    RUNNING              4   adam            tanh                                  128                 64                  5          0.000409061        122                                              │
│ trial_f7582    RUNNING              2   rmsprop         relu                                   32                 64                  5          0.00254553          76                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.00057906         134                                              │
│ trial_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                  128                 64                  3          1.12827e-05         79                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 64                  5          2.65327e-05        102                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                 64                  3          0.0011964          137                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                  128                 32                  3          8.15724e-05         95                                              │
│ trial_f7582    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00153955          78                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82                                              │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1301559)[0m 
[1m69/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 27ms/step - accuracy: 0.3144 - loss: 1.4598
[1m71/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 27ms/step - accuracy: 0.3145 - loss: 1.4595[32m [repeated 104x across cluster][0m
[36m(train_cnn_ray_tune pid=1301559)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 25ms/step - accuracy: 0.2995 - loss: 1.5016 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[1m30/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 57ms/step - accuracy: 0.3738 - loss: 1.3003
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[36m(train_cnn_ray_tune pid=1301551)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.2474 - loss: 1.7128 
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[36m(train_cnn_ray_tune pid=1301551)[0m Epoch 15/126[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m Epoch 38/95[32m [repeated 15x across cluster][0m
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301518)[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=1301518)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301518)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:35:18. Total running time: 2min 40s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             157.779 │
│ time_total_s                 157.779 │
│ training_iteration                 1 │
│ val_accuracy                 0.48193 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:35:18. Total running time: 2min 40s
[36m(train_cnn_ray_tune pid=1301518)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:35:21. Total running time: 2min 42s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             159.644 │
│ time_total_s                 159.644 │
│ training_iteration                 1 │
│ val_accuracy                 0.40375 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:35:21. Total running time: 2min 42s
[36m(train_cnn_ray_tune pid=1301507)[0m Epoch 21/122[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301559)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301543)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:35:25. Total running time: 2min 47s
[36m(train_cnn_ray_tune pid=1301543)[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=1301543)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             164.422 │
│ time_total_s                 164.422 │
│ training_iteration                 1 │
│ val_accuracy                 0.62385 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:35:25. Total running time: 2min 47s
[36m(train_cnn_ray_tune pid=1301556)[0m Epoch 36/134[32m [repeated 11x across cluster][0m
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m Epoch 40/79[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 89ms/step - accuracy: 0.3203 - loss: 1.7381[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 261ms/step
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m 8/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step   
[1m14/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m21/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m33/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1301566)[0m Epoch 20/89[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m38/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step
[1m44/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
[1m 7/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m13/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=1301553)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3359 - loss: 1.3642 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3349 - loss: 1.3700[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 96ms/step - accuracy: 0.3828 - loss: 1.3078
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301554)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:35:37. Total running time: 2min 59s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             176.562 │
│ time_total_s                 176.562 │
│ training_iteration                 1 │
│ val_accuracy                  0.6117 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:35:37. Total running time: 2min 59s
[36m(train_cnn_ray_tune pid=1301554)[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=1301554)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301554)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-11-04 12:35:38. Total running time: 3min 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_f7582    RUNNING              4   adam            relu                                   32                 64                  5          0.000844071         78                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 32                  5          3.84427e-05        126                                              │
│ trial_f7582    RUNNING              4   adam            tanh                                  128                 64                  5          0.000409061        122                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.00057906         134                                              │
│ trial_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                  128                 64                  3          1.12827e-05         79                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 64                  5          2.65327e-05        102                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00153955          78                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82                                              │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 64                  3          0.00129301          89        1           157.779          0.481932 │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   rmsprop         relu                                   32                 64                  5          0.00254553          76        1           176.562          0.611695 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                 64                  3          0.0011964          137        1           164.422          0.62385  │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
│ trial_f7582    TERMINATED           2   adam            tanh                                  128                 32                  3          8.15724e-05         95        1           159.644          0.403745 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m Epoch 34/82[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m Epoch 22/89[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[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=1301534)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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[36m(train_cnn_ray_tune pid=1301534)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:35:50. Total running time: 3min 12s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             189.566 │
│ time_total_s                 189.566 │
│ training_iteration                 1 │
│ val_accuracy                 0.47208 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:35:50. Total running time: 3min 12s
[36m(train_cnn_ray_tune pid=1301560)[0m Epoch 37/82[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 19ms/step - accuracy: 0.3597 - loss: 1.3667
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 106ms/step - accuracy: 0.4766 - loss: 1.1503
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 47ms/step - accuracy: 0.4197 - loss: 1.1967 [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
[1m53/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 26ms/step - accuracy: 0.3417 - loss: 1.6644
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[36m(train_cnn_ray_tune pid=1301556)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 72ms/step - accuracy: 0.5625 - loss: 1.0250[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.3070 - loss: 1.4372 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.3122 - loss: 1.4332
[36m(train_cnn_ray_tune pid=1301566)[0m 
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 26ms/step - accuracy: 0.3698 - loss: 1.5936[32m [repeated 135x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 26ms/step - accuracy: 0.3698 - loss: 1.5938
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 26ms/step - accuracy: 0.3697 - loss: 1.5939[32m [repeated 175x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3923 - loss: 1.6476 
[36m(train_cnn_ray_tune pid=1301507)[0m Epoch 29/122[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301552)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3212 - loss: 1.3436
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3490 - loss: 1.3067
[36m(train_cnn_ray_tune pid=1301567)[0m 
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 17ms/step - accuracy: 0.3298 - loss: 1.6252
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 17ms/step - accuracy: 0.3297 - loss: 1.6250
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m Epoch 25/89[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[1m 7/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.3959 - loss: 1.1926
[1m 9/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.3983 - loss: 1.1950
[36m(train_cnn_ray_tune pid=1301532)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 97ms/step - accuracy: 0.4141 - loss: 1.3848[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1301532)[0m 
[1m13/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3392 - loss: 1.6749
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 19ms/step - accuracy: 0.3559 - loss: 1.3527[32m [repeated 196x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3841 - loss: 1.5815 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3662 - loss: 1.3272 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1301532)[0m Epoch 54/79[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 98ms/step - accuracy: 0.3203 - loss: 1.2297
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 30ms/step - accuracy: 0.3581 - loss: 1.2198[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1301526)[0m 
[1m236/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 25ms/step - accuracy: 0.4809 - loss: 1.0797
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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Trial status: 11 RUNNING | 9 TERMINATED
Current time: 2025-11-04 12:36:08. Total running time: 3min 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_f7582    RUNNING              4   adam            relu                                   32                 64                  5          0.000844071         78                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 32                  5          3.84427e-05        126                                              │
│ trial_f7582    RUNNING              4   adam            tanh                                  128                 64                  5          0.000409061        122                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.00057906         134                                              │
│ trial_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                  128                 64                  3          1.12827e-05         79                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 64                  5          2.65327e-05        102                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82                                              │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 64                  3          0.00129301          89        1           157.779          0.481932 │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   rmsprop         relu                                   32                 64                  5          0.00254553          76        1           176.562          0.611695 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                 64                  3          0.0011964          137        1           164.422          0.62385  │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
│ trial_f7582    TERMINATED           2   adam            tanh                                  128                 32                  3          8.15724e-05         95        1           159.644          0.403745 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00153955          78        1           189.566          0.472076 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m Epoch 33/122[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m Epoch 45/82[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 38/122[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m Epoch 27/126[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 390ms/step
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[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=1301526)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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[36m(train_cnn_ray_tune pid=1301526)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:36:31. Total running time: 3min 53s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             230.517 │
│ time_total_s                 230.517 │
│ training_iteration                 1 │
│ val_accuracy                 0.48784 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:36:31. Total running time: 3min 53s
[36m(train_cnn_ray_tune pid=1301551)[0m Epoch 28/126[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 109ms/step - accuracy: 0.4766 - loss: 1.1625[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m Epoch 52/82[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 256ms/step
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
[1m24/49[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 88ms/step - accuracy: 0.4219 - loss: 1.1436
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[36m(train_cnn_ray_tune pid=1301551)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 43ms/step
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[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=1301551)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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[36m(train_cnn_ray_tune pid=1301551)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:36:38. Total running time: 4min 0s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             237.465 │
│ time_total_s                 237.465 │
│ training_iteration                 1 │
│ val_accuracy                 0.39126 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:36:38. Total running time: 4min 0s
[36m(train_cnn_ray_tune pid=1301551)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
[36m(train_cnn_ray_tune pid=1301566)[0m 
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Trial status: 11 TERMINATED | 9 RUNNING
Current time: 2025-11-04 12:36:38. Total running time: 4min 0s
Logical resource usage: 9.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_f7582    RUNNING              4   adam            tanh                                  128                 64                  5          0.000409061        122                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.00057906         134                                              │
│ trial_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                  128                 64                  3          1.12827e-05         79                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 64                  5          2.65327e-05        102                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82                                              │
│ trial_f7582    TERMINATED           4   adam            relu                                   32                 64                  5          0.000844071         78        1           230.517          0.487845 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 64                  3          0.00129301          89        1           157.779          0.481932 │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 32                  5          3.84427e-05        126        1           237.465          0.391261 │
│ trial_f7582    TERMINATED           2   rmsprop         relu                                   32                 64                  5          0.00254553          76        1           176.562          0.611695 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                 64                  3          0.0011964          137        1           164.422          0.62385  │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
│ trial_f7582    TERMINATED           2   adam            tanh                                  128                 32                  3          8.15724e-05         95        1           159.644          0.403745 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00153955          78        1           189.566          0.472076 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m Epoch 65/134[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m Epoch 35/89[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m Epoch 14/84[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[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=1301507)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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[36m(train_cnn_ray_tune pid=1301507)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:36:54. Total running time: 4min 16s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              253.24 │
│ time_total_s                  253.24 │
│ training_iteration                 1 │
│ val_accuracy                 0.44218 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:36:54. Total running time: 4min 16s
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m Epoch 77/79[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[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=1301532)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m Epoch 75/134[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1301532)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 230ms/step
[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301532)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:37:03. Total running time: 4min 25s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             262.285 │
│ time_total_s                 262.285 │
│ training_iteration                 1 │
│ val_accuracy                 0.46452 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:37:03. Total running time: 4min 25s
[36m(train_cnn_ray_tune pid=1301532)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 59ms/step - accuracy: 0.3906 - loss: 1.4390[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m Epoch 41/89[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[1m 92/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.3485 - loss: 1.4949
[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[1m126/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.3493 - loss: 1.4901
[36m(train_cnn_ray_tune pid=1301556)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step - accuracy: 0.6186 - loss: 0.8838 - val_accuracy: 0.6193 - val_loss: 0.8049[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1301555)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 66ms/step - accuracy: 0.3750 - loss: 1.2489
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3984 - loss: 1.2430 [32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1301567)[0m 
[1m131/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.3496 - loss: 1.4893
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[1m156/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.3500 - loss: 1.4867
[36m(train_cnn_ray_tune pid=1301555)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 22ms/step - accuracy: 0.4034 - loss: 1.2626 - val_accuracy: 0.4172 - val_loss: 1.2194[32m [repeated 8x across cluster][0m

Trial status: 13 TERMINATED | 7 RUNNING
Current time: 2025-11-04 12:37:08. Total running time: 4min 30s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.00057906         134                                              │
│ trial_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 64                  5          2.65327e-05        102                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    RUNNING              3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82                                              │
│ trial_f7582    TERMINATED           4   adam            relu                                   32                 64                  5          0.000844071         78        1           230.517          0.487845 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 64                  3          0.00129301          89        1           157.779          0.481932 │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 32                  5          3.84427e-05        126        1           237.465          0.391261 │
│ trial_f7582    TERMINATED           4   adam            tanh                                  128                 64                  5          0.000409061        122        1           253.24           0.442181 │
│ trial_f7582    TERMINATED           2   rmsprop         relu                                   32                 64                  5          0.00254553          76        1           176.562          0.611695 │
│ trial_f7582    TERMINATED           2   adam            relu                                  128                 64                  3          1.12827e-05         79        1           262.285          0.46452  │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                 64                  3          0.0011964          137        1           164.422          0.62385  │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
│ trial_f7582    TERMINATED           2   adam            tanh                                  128                 32                  3          8.15724e-05         95        1           159.644          0.403745 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00153955          78        1           189.566          0.472076 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m Epoch 32/104[32m [repeated 13x across cluster][0m
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301556)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:37:14. Total running time: 4min 36s
[36m(train_cnn_ray_tune pid=1301556)[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=1301556)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             273.196 │
│ time_total_s                 273.196 │
│ training_iteration                 1 │
│ val_accuracy                 0.61498 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:37:14. Total running time: 4min 36s
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 57/122[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 63ms/step - accuracy: 0.5312 - loss: 1.0176[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1301560)[0m 
[1m43/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 15ms/step - accuracy: 0.4913 - loss: 1.0741
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 16ms/step - accuracy: 0.4090 - loss: 1.2422[32m [repeated 43x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
[1m 52/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step - accuracy: 0.3954 - loss: 1.4594
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[36m(train_cnn_ray_tune pid=1301553)[0m 
[1m 58/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.4268 - loss: 1.2281 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 10ms/step - accuracy: 0.3553 - loss: 1.4448 - val_accuracy: 0.4005 - val_loss: 1.2300[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
[1m47/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 16ms/step - accuracy: 0.5137 - loss: 1.0665
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[36m(train_cnn_ray_tune pid=1301567)[0m Epoch 34/102[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 62/122[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m Epoch 51/89[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[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=1301560)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m Epoch 40/104[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301560)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:37:38. Total running time: 5min 0s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             297.516 │
│ time_total_s                 297.516 │
│ training_iteration                 1 │
│ val_accuracy                  0.5634 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:37:38. Total running time: 5min 0s

Trial status: 15 TERMINATED | 5 RUNNING
Current time: 2025-11-04 12:37:38. Total running time: 5min 0s
Logical resource usage: 5.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   32                 64                  5          2.65327e-05        102                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    TERMINATED           4   adam            relu                                   32                 64                  5          0.000844071         78        1           230.517          0.487845 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 64                  3          0.00129301          89        1           157.779          0.481932 │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 32                  5          3.84427e-05        126        1           237.465          0.391261 │
│ trial_f7582    TERMINATED           4   adam            tanh                                  128                 64                  5          0.000409061        122        1           253.24           0.442181 │
│ trial_f7582    TERMINATED           2   rmsprop         relu                                   32                 64                  5          0.00254553          76        1           176.562          0.611695 │
│ trial_f7582    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.00057906         134        1           273.196          0.61498  │
│ trial_f7582    TERMINATED           2   adam            relu                                  128                 64                  3          1.12827e-05         79        1           262.285          0.46452  │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                 64                  3          0.0011964          137        1           164.422          0.62385  │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
│ trial_f7582    TERMINATED           2   adam            tanh                                  128                 32                  3          8.15724e-05         95        1           159.644          0.403745 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00153955          78        1           189.566          0.472076 │
│ trial_f7582    TERMINATED           3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82        1           297.516          0.563403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301567)[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=1301567)[0m   _log_deprecation_warning(
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[36m(train_cnn_ray_tune pid=1301567)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:37:48. Total running time: 5min 9s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             306.732 │
│ time_total_s                 306.732 │
│ training_iteration                 1 │
│ val_accuracy                 0.40079 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:37:48. Total running time: 5min 9s
[36m(train_cnn_ray_tune pid=1301567)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 77/122[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m Epoch 26/84[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m Epoch 51/104[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 88/122[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301553)[0m 
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Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-04 12:38:09. Total running time: 5min 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_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    TERMINATED           4   adam            relu                                   32                 64                  5          0.000844071         78        1           230.517          0.487845 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 64                  3          0.00129301          89        1           157.779          0.481932 │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 32                  5          3.84427e-05        126        1           237.465          0.391261 │
│ trial_f7582    TERMINATED           4   adam            tanh                                  128                 64                  5          0.000409061        122        1           253.24           0.442181 │
│ trial_f7582    TERMINATED           2   rmsprop         relu                                   32                 64                  5          0.00254553          76        1           176.562          0.611695 │
│ trial_f7582    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.00057906         134        1           273.196          0.61498  │
│ trial_f7582    TERMINATED           2   adam            relu                                  128                 64                  3          1.12827e-05         79        1           262.285          0.46452  │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 64                  5          2.65327e-05        102        1           306.732          0.400788 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                 64                  3          0.0011964          137        1           164.422          0.62385  │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
│ trial_f7582    TERMINATED           2   adam            tanh                                  128                 32                  3          8.15724e-05         95        1           159.644          0.403745 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00153955          78        1           189.566          0.472076 │
│ trial_f7582    TERMINATED           3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82        1           297.516          0.563403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1301552)[0m 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 11ms/step - accuracy: 0.5093 - loss: 1.1545 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m Epoch 73/89[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m Epoch 58/104[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 99/122[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 103/122[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 106/122[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 110/122[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301566)[0m 
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Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-04 12:38:39. Total running time: 6min 1s
Logical resource usage: 4.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_f7582    RUNNING              3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104                                              │
│ trial_f7582    RUNNING              4   adam            relu                                   32                128                  3          0.000172281         84                                              │
│ trial_f7582    RUNNING              2   adam            relu                                   64                128                  3          1.11943e-05         89                                              │
│ trial_f7582    RUNNING              2   adam            tanh                                   64                 64                  3          6.55492e-05        122                                              │
│ trial_f7582    TERMINATED           4   adam            relu                                   32                 64                  5          0.000844071         78        1           230.517          0.487845 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 64                  3          0.00129301          89        1           157.779          0.481932 │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 32                  5          3.84427e-05        126        1           237.465          0.391261 │
│ trial_f7582    TERMINATED           4   adam            tanh                                  128                 64                  5          0.000409061        122        1           253.24           0.442181 │
│ trial_f7582    TERMINATED           2   rmsprop         relu                                   32                 64                  5          0.00254553          76        1           176.562          0.611695 │
│ trial_f7582    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.00057906         134        1           273.196          0.61498  │
│ trial_f7582    TERMINATED           2   adam            relu                                  128                 64                  3          1.12827e-05         79        1           262.285          0.46452  │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 64                  5          2.65327e-05        102        1           306.732          0.400788 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                 64                  3          0.0011964          137        1           164.422          0.62385  │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
│ trial_f7582    TERMINATED           2   adam            tanh                                  128                 32                  3          8.15724e-05         95        1           159.644          0.403745 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00153955          78        1           189.566          0.472076 │
│ trial_f7582    TERMINATED           3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82        1           297.516          0.563403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1301566)[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=1301566)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:38:40. Total running time: 6min 2s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             359.508 │
│ time_total_s                 359.508 │
│ training_iteration                 1 │
│ val_accuracy                 0.53318 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:38:40. Total running time: 6min 2s
[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m Epoch 38/84[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[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=1301555)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301555)[0m 
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[36m(train_cnn_ray_tune pid=1301553)[0m Epoch 119/122[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1301552)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:38:51. Total running time: 6min 12s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             369.937 │
│ time_total_s                 369.937 │
│ training_iteration                 1 │
│ val_accuracy                  0.4724 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:38:51. Total running time: 6min 12s
[36m(train_cnn_ray_tune pid=1301555)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:38:52. Total running time: 6min 13s
[36m(train_cnn_ray_tune pid=1301553)[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=1301553)[0m   _log_deprecation_warning(
2025-11-04 12:38:57,159	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_gyr_superclasses_CPA_METs/ESANN_hyperparameters_tuning' in 0.0067s.
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             371.054 │
│ time_total_s                 371.054 │
│ training_iteration                 1 │
│ val_accuracy                 0.45729 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:38:52. Total running time: 6min 14s
[36m(train_cnn_ray_tune pid=1301553)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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Trial trial_f7582 finished iteration 1 at 2025-11-04 12:38:57. Total running time: 6min 19s
╭──────────────────────────────────────╮
│ Trial trial_f7582 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              376.09 │
│ time_total_s                  376.09 │
│ training_iteration                 1 │
│ val_accuracy                 0.58246 │
╰──────────────────────────────────────╯

Trial trial_f7582 completed after 1 iterations at 2025-11-04 12:38:57. Total running time: 6min 19s

Trial status: 20 TERMINATED
Current time: 2025-11-04 12:38:57. Total running time: 6min 19s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
/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 00:00:1762256337.291201 1299904 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
[36m(train_cnn_ray_tune pid=1301552)[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=1301552)[0m   _log_deprecation_warning(
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_f7582    TERMINATED           4   adam            relu                                   32                 64                  5          0.000844071         78        1           230.517          0.487845 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 64                  3          0.00129301          89        1           157.779          0.481932 │
│ trial_f7582    TERMINATED           4   rmsprop         tanh                                   64                 64                  5          1.31274e-05        105        1            94.9735         0.384691 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.00124114         128        1           116.78           0.438568 │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 32                  5          3.84427e-05        126        1           237.465          0.391261 │
│ trial_f7582    TERMINATED           4   adam            tanh                                  128                 64                  5          0.000409061        122        1           253.24           0.442181 │
│ trial_f7582    TERMINATED           2   rmsprop         relu                                   32                 64                  5          0.00254553          76        1           176.562          0.611695 │
│ trial_f7582    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.00057906         134        1           273.196          0.61498  │
│ trial_f7582    TERMINATED           3   rmsprop         tanh                                   64                128                  5          8.16381e-05        104        1           369.937          0.472405 │
│ trial_f7582    TERMINATED           4   adam            relu                                   32                128                  3          0.000172281         84        1           376.09           0.582457 │
│ trial_f7582    TERMINATED           2   adam            relu                                  128                 64                  3          1.12827e-05         79        1           262.285          0.46452  │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.00148034          65        1           128.338          0.448752 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                128                  3          1.11943e-05         89        1           359.508          0.53318  │
│ trial_f7582    TERMINATED           2   adam            tanh                                   32                 64                  5          2.65327e-05        102        1           306.732          0.400788 │
│ trial_f7582    TERMINATED           2   adam            relu                                   64                 64                  3          0.0011964          137        1           164.422          0.62385  │
│ trial_f7582    TERMINATED           2   adam            tanh                                   64                 64                  3          6.55492e-05        122        1           371.054          0.457293 │
│ trial_f7582    TERMINATED           4   rmsprop         relu                                   32                 64                  5          6.35355e-05         92        1           121.008          0.404074 │
│ trial_f7582    TERMINATED           2   adam            tanh                                  128                 32                  3          8.15724e-05         95        1           159.644          0.403745 │
│ trial_f7582    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00153955          78        1           189.566          0.472076 │
│ trial_f7582    TERMINATED           3   rmsprop         relu                                  128                 64                  5          9.27079e-05         82        1           297.516          0.563403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 64, 'numero_filtros': 64, 'tamanho_filtro': 3, 'tasa_aprendizaje': 0.0011964044175510873, 'epochs': 137}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256339.116695 1388545 service.cc:152] XLA service 0x7cbc98013560 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256339.116726 1388545 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:38:59.150657: 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 00:00:1762256339.318369 1388545 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256341.537779 1388545 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 2/137

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Epoch 3/137

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[1m105/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4186 - loss: 1.2229
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4198 - loss: 1.2191
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Epoch 4/137

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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4580 - loss: 1.1644
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1610
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Epoch 5/137

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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.1097
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Epoch 6/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0837 
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[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0802
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0766
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Epoch 7/137

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Epoch 8/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0412 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 1.0231
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5544 - loss: 1.0190
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5548 - loss: 1.0151
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Epoch 9/137

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[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5593 - loss: 0.9647 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5668 - loss: 0.9678
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5688 - loss: 0.9682
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5701 - loss: 0.9686
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5703 - loss: 0.9690 - val_accuracy: 0.5913 - val_loss: 0.8916
Epoch 10/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5782 - loss: 0.9563 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5804 - loss: 0.9469
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[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5811 - loss: 0.9441
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5807 - loss: 0.9446 - val_accuracy: 0.5890 - val_loss: 0.8658
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9010
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5946 - loss: 0.9181 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5918 - loss: 0.9205
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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5920 - loss: 0.9205
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5922 - loss: 0.9208 - val_accuracy: 0.5949 - val_loss: 0.8440
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.9018
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6022 - loss: 0.9157 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6054 - loss: 0.9076
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6060 - loss: 0.9072
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6058 - loss: 0.9066
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6055 - loss: 0.9065 - val_accuracy: 0.6015 - val_loss: 0.8395
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0021
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6168 - loss: 0.9154 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6082 - loss: 0.9195
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6067 - loss: 0.9172
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6070 - loss: 0.9139
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6071 - loss: 0.9124 - val_accuracy: 0.6147 - val_loss: 0.8267
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8171
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6034 - loss: 0.8821 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6079 - loss: 0.8851
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6086 - loss: 0.8863
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6088 - loss: 0.8873
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6087 - loss: 0.8875 - val_accuracy: 0.6222 - val_loss: 0.8214
Epoch 15/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6265 - loss: 0.8457 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6240 - loss: 0.8509
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6245 - loss: 0.8539
[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6243 - loss: 0.8561
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Epoch 16/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6187 - loss: 0.8521 
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Epoch 17/137

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Epoch 18/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6193 - loss: 0.8596
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Epoch 19/137

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[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6299 - loss: 0.8566 
[1m 69/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6271 - loss: 0.8505
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[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6230 - loss: 0.8509
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Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 1.0880
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6346 - loss: 0.8590 
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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6321 - loss: 0.8423
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6319 - loss: 0.8391
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6319 - loss: 0.8387 - val_accuracy: 0.5992 - val_loss: 0.8355
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7444
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6337 - loss: 0.8101 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6315 - loss: 0.8180
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6329 - loss: 0.8176
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6335 - loss: 0.8186
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6337 - loss: 0.8191 - val_accuracy: 0.6186 - val_loss: 0.8073
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9052
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6333 - loss: 0.8256 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6396 - loss: 0.8158
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6421 - loss: 0.8115
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6418 - loss: 0.8120
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6415 - loss: 0.8126 - val_accuracy: 0.6202 - val_loss: 0.7957
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6875 - loss: 0.7206
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6453 - loss: 0.8055 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6453 - loss: 0.8052
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6455 - loss: 0.8061
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6450 - loss: 0.8065
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Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7189
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6496 - loss: 0.7819 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6464 - loss: 0.7925
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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6413 - loss: 0.8026
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Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9407
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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6399 - loss: 0.8164
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6404 - loss: 0.8141
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6415 - loss: 0.8126
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6420 - loss: 0.8118 - val_accuracy: 0.6166 - val_loss: 0.8075
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.7119
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6447 - loss: 0.7746 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6410 - loss: 0.7867
[1m104/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6413 - loss: 0.7903
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6413 - loss: 0.7934
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6416 - loss: 0.7943 - val_accuracy: 0.6261 - val_loss: 0.8052
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5781 - loss: 0.8814
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6502 - loss: 0.8050 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6474 - loss: 0.8000
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6475 - loss: 0.7947
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6479 - loss: 0.7932
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6482 - loss: 0.7925 - val_accuracy: 0.6222 - val_loss: 0.8112

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 656ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.
[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m Epoch 43/84
[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
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[36m(train_cnn_ray_tune pid=1301552)[0m 
[1m93/96[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step[32m [repeated 3x across cluster][0m

=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:49[0m 871ms/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m71/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 718us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.61 [%]
Global F1 score (validation) = 62.75 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.51823026 0.41926876 0.03534069 0.02716024]
 [0.3942235  0.55263716 0.00485964 0.04827968]
 [0.44533747 0.4530427  0.02048838 0.0811315 ]
 ...
 [0.02320043 0.00711881 0.96202403 0.00765669]
 [0.02219048 0.00675332 0.96385497 0.00720126]
 [0.06892361 0.02611247 0.88598156 0.01898234]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.98 [%]
Global accuracy score (test) = 57.54 [%]
Global F1 score (train) = 67.75 [%]
Global F1 score (test) = 57.94 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.39      0.38       400
MODERATE-INTENSITY       0.45      0.45      0.45       400
         SEDENTARY       0.70      0.87      0.78       400
VIGOROUS-INTENSITY       0.88      0.60      0.71       345

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

2025-11-04 12:39:27.763906: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:39:27.775108: 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 00:00:1762256367.788189 1391925 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256367.792119 1391925 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256367.802047 1391925 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256367.802064 1391925 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256367.802066 1391925 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256367.802068 1391925 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:39:27.805294: 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 00:00:1762256370.202800 1391925 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256371.853944 1392058 service.cc:152] XLA service 0x7e15b80027e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256371.853969 1392058 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:39:31.887576: 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 00:00:1762256372.052281 1392058 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256374.308777 1392058 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:29[0m 3s/step - accuracy: 0.2031 - loss: 2.2846
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2638 - loss: 2.1117 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 2.0330
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2822 - loss: 1.9606
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2900 - loss: 1.9007
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2940 - loss: 1.8703
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2941 - loss: 1.8690 - val_accuracy: 0.4593 - val_loss: 1.1336
Epoch 2/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 1.3672
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3768 - loss: 1.3816 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3776 - loss: 1.3676
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3800 - loss: 1.3573
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3826 - loss: 1.3484
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3837 - loss: 1.3435 - val_accuracy: 0.5053 - val_loss: 1.0990
Epoch 3/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4039 - loss: 1.2467 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4133 - loss: 1.2363
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4188 - loss: 1.2287
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4236 - loss: 1.2219
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4246 - loss: 1.2204 - val_accuracy: 0.5539 - val_loss: 1.0477
Epoch 4/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4589 - loss: 1.1609 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1552
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1525
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1499
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4669 - loss: 1.1486 - val_accuracy: 0.5532 - val_loss: 1.0088
Epoch 5/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1022 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1041
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1047
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1039
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Epoch 6/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.0550 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0485
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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0477
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5199 - loss: 1.0476 - val_accuracy: 0.5782 - val_loss: 0.9350
Epoch 7/137

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[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0357
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5443 - loss: 1.0307
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5451 - loss: 1.0289 - val_accuracy: 0.5769 - val_loss: 0.9133
Epoch 8/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5829 - loss: 0.9932 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5765 - loss: 0.9921
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5735 - loss: 0.9897
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5710 - loss: 0.9877
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5705 - loss: 0.9871 - val_accuracy: 0.5920 - val_loss: 0.8745
Epoch 9/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5942 - loss: 0.9357 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5939 - loss: 0.9361
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5913 - loss: 0.9395
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5895 - loss: 0.9417
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5891 - loss: 0.9421 - val_accuracy: 0.6199 - val_loss: 0.8419
Epoch 10/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6131 - loss: 0.9158 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6070 - loss: 0.9243
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6014 - loss: 0.9268
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5983 - loss: 0.9281
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5975 - loss: 0.9287 - val_accuracy: 0.6258 - val_loss: 0.8304
Epoch 11/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5971 - loss: 0.8968 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5938 - loss: 0.9081
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5948 - loss: 0.9100
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5954 - loss: 0.9100
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5956 - loss: 0.9098 - val_accuracy: 0.6015 - val_loss: 0.8366
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 0.8781
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5781 - loss: 0.9007 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5814 - loss: 0.9053
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5852 - loss: 0.9037
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5888 - loss: 0.9024
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Epoch 13/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6376 - loss: 0.8722 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6278 - loss: 0.8724
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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6209 - loss: 0.8778
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6200 - loss: 0.8783 - val_accuracy: 0.6176 - val_loss: 0.8172
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.8415
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5873 - loss: 0.8814 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5977 - loss: 0.8792
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6000 - loss: 0.8796
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6017 - loss: 0.8797
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6024 - loss: 0.8792 - val_accuracy: 0.6219 - val_loss: 0.8115
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8757
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6138 - loss: 0.8615 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6130 - loss: 0.8668
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6137 - loss: 0.8673
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6142 - loss: 0.8660
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6143 - loss: 0.8654 - val_accuracy: 0.6255 - val_loss: 0.8036
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6875 - loss: 0.8455
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6315 - loss: 0.8202 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6228 - loss: 0.8377
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6214 - loss: 0.8414
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6209 - loss: 0.8432
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6210 - loss: 0.8434 - val_accuracy: 0.6229 - val_loss: 0.8069
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6250 - loss: 0.9006
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6280 - loss: 0.8796 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6269 - loss: 0.8687
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6269 - loss: 0.8609
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6262 - loss: 0.8577
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6261 - loss: 0.8569 - val_accuracy: 0.6173 - val_loss: 0.8100
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5469 - loss: 1.0315
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6222 - loss: 0.8453 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6295 - loss: 0.8302
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6318 - loss: 0.8249
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6321 - loss: 0.8235
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6317 - loss: 0.8238 - val_accuracy: 0.6219 - val_loss: 0.8136
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.0154
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6194 - loss: 0.8739 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6218 - loss: 0.8603
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6236 - loss: 0.8547
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6255 - loss: 0.8488
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6267 - loss: 0.8458 - val_accuracy: 0.6229 - val_loss: 0.8151
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 0.8706
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6233 - loss: 0.7914 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6295 - loss: 0.7995
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6291 - loss: 0.8059
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6282 - loss: 0.8100
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6280 - loss: 0.8108 - val_accuracy: 0.6202 - val_loss: 0.8046

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 814ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 1: 57.54 [%]
F1-score capturado en la ejecución 1: 57.94 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:51[0m 878ms/step
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[1m126/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 812us/step
[1m200/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 763us/step
[1m279/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 727us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 748us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.71 [%]
Global F1 score (validation) = 62.55 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4219889  0.33148062 0.11095158 0.1355789 ]
 [0.45534152 0.51199514 0.01311818 0.01954519]
 [0.42874947 0.5315341  0.00959855 0.03011797]
 ...
 [0.04529653 0.02015889 0.91993093 0.01461369]
 [0.03050983 0.01274417 0.94751585 0.00923014]
 [0.03143988 0.01312761 0.945403   0.01002949]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 66.34 [%]
Global accuracy score (test) = 60.26 [%]
Global F1 score (train) = 65.4 [%]
Global F1 score (test) = 60.24 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.33      0.36       400
MODERATE-INTENSITY       0.47      0.53      0.50       400
         SEDENTARY       0.71      0.91      0.80       400
VIGOROUS-INTENSITY       0.90      0.64      0.75       345

          accuracy                           0.60      1545
         macro avg       0.62      0.60      0.60      1545
      weighted avg       0.61      0.60      0.60      1545

2025-11-04 12:39:55.863085: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:39:55.874570: 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 00:00:1762256395.888043 1394749 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256395.892288 1394749 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256395.902158 1394749 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256395.902179 1394749 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256395.902181 1394749 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256395.902182 1394749 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:39:55.905435: 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 00:00:1762256398.285941 1394749 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256399.927418 1394857 service.cc:152] XLA service 0x717a7000cf10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256399.927449 1394857 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:39:59.960875: 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 00:00:1762256400.125224 1394857 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256402.321031 1394857 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:19[0m 3s/step - accuracy: 0.2656 - loss: 2.2887
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2575 - loss: 2.1679 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2758 - loss: 2.0378
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2864 - loss: 1.9611
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2940 - loss: 1.9023
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2978 - loss: 1.8737
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2979 - loss: 1.8724 - val_accuracy: 0.4934 - val_loss: 1.1134
Epoch 2/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3722 - loss: 1.3596 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3756 - loss: 1.3563
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3786 - loss: 1.3485
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3814 - loss: 1.3405
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Epoch 3/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4343 - loss: 1.2366 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4346 - loss: 1.2273
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Epoch 4/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1487 
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[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1485
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1473
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Epoch 5/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1080 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1003
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1002
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.0988
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Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0195
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0585 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0619
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0594
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0578
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5231 - loss: 1.0571 - val_accuracy: 0.5936 - val_loss: 0.9172
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0701
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0371 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0343
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0324
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5416 - loss: 1.0316
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5420 - loss: 1.0307 - val_accuracy: 0.5995 - val_loss: 0.8959
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 0.9529
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5501 - loss: 1.0104 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 1.0012
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 0.9958
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5590 - loss: 0.9938
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5595 - loss: 0.9932 - val_accuracy: 0.6078 - val_loss: 0.8732
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.8989
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5888 - loss: 0.9867 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5833 - loss: 0.9767
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5801 - loss: 0.9728
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5779 - loss: 0.9708
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Epoch 10/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5775 - loss: 0.9496 
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Epoch 11/137

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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5885 - loss: 0.9318
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Epoch 12/137

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[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5958 - loss: 0.9111
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Epoch 13/137

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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6048 - loss: 0.8826
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Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.6094 - loss: 0.7065
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6069 - loss: 0.8681 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6078 - loss: 0.8763
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6074 - loss: 0.8782
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6072 - loss: 0.8791
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6073 - loss: 0.8791 - val_accuracy: 0.6163 - val_loss: 0.8217
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6562 - loss: 0.6807
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6139 - loss: 0.8643 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6119 - loss: 0.8678
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6122 - loss: 0.8670
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6126 - loss: 0.8678
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6128 - loss: 0.8679 - val_accuracy: 0.6156 - val_loss: 0.8299
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7958
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6239 - loss: 0.8320 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6235 - loss: 0.8410
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6227 - loss: 0.8456
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6220 - loss: 0.8485
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6213 - loss: 0.8497 - val_accuracy: 0.6038 - val_loss: 0.8167
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9544
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6094 - loss: 0.8464 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6116 - loss: 0.8508
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6123 - loss: 0.8498
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6125 - loss: 0.8495
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6126 - loss: 0.8496 - val_accuracy: 0.6242 - val_loss: 0.8215
Epoch 18/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6362 - loss: 0.8404 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6337 - loss: 0.8369
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[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6299 - loss: 0.8361
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Epoch 19/137

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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6260 - loss: 0.8334
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6250 - loss: 0.8350
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Epoch 20/137

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Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7380
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6518 - loss: 0.7963 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6471 - loss: 0.8000
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[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6437 - loss: 0.8060
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Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.7985
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6139 - loss: 0.8409 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6205 - loss: 0.8287
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6212 - loss: 0.8243
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6223 - loss: 0.8231
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6226 - loss: 0.8228 - val_accuracy: 0.6202 - val_loss: 0.8180
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9480
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6256 - loss: 0.8366 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6259 - loss: 0.8346
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6279 - loss: 0.8301
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6294 - loss: 0.8277
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6297 - loss: 0.8270 - val_accuracy: 0.6284 - val_loss: 0.8046
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5156 - loss: 0.8596
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6293 - loss: 0.7976 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6300 - loss: 0.8020
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6319 - loss: 0.8042
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6338 - loss: 0.8037
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6341 - loss: 0.8039 - val_accuracy: 0.6150 - val_loss: 0.8110
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 0.9792
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6280 - loss: 0.8234 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6347 - loss: 0.8127
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6353 - loss: 0.8094
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6348 - loss: 0.8082
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6346 - loss: 0.8082 - val_accuracy: 0.6314 - val_loss: 0.8061
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7500 - loss: 0.7993
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6600 - loss: 0.7922 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6529 - loss: 0.7915
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6510 - loss: 0.7912
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6491 - loss: 0.7920
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Epoch 27/137

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Epoch 28/137

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[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6417 - loss: 0.7874
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Epoch 29/137

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[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6426 - loss: 0.7873
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6444 - loss: 0.7871
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Epoch 30/137

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[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6490 - loss: 0.7666
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6484 - loss: 0.7685
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6483 - loss: 0.7701
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Epoch 31/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7168
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6494 - loss: 0.7834 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6479 - loss: 0.7825
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6463 - loss: 0.7828
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6457 - loss: 0.7830
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6455 - loss: 0.7829 - val_accuracy: 0.6186 - val_loss: 0.8186
Epoch 32/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8356
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6582 - loss: 0.7405 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6555 - loss: 0.7521
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6539 - loss: 0.7598
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6527 - loss: 0.7656
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6523 - loss: 0.7676 - val_accuracy: 0.6117 - val_loss: 0.7997
Epoch 33/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6719 - loss: 0.7736
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6647 - loss: 0.7451 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6618 - loss: 0.7519
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6589 - loss: 0.7577
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6577 - loss: 0.7607
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6575 - loss: 0.7618 - val_accuracy: 0.6094 - val_loss: 0.7925
Epoch 34/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7344 - loss: 0.7100
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6524 - loss: 0.7374 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6474 - loss: 0.7469
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6484 - loss: 0.7480
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Epoch 35/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6192 - loss: 0.7851 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6302 - loss: 0.7800
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6344 - loss: 0.7779
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6364 - loss: 0.7757
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6372 - loss: 0.7751 - val_accuracy: 0.6147 - val_loss: 0.8177
Epoch 36/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0008
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6272 - loss: 0.8092 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6342 - loss: 0.7947
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6383 - loss: 0.7860
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6412 - loss: 0.7800
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6418 - loss: 0.7786 - val_accuracy: 0.6209 - val_loss: 0.8083
Epoch 37/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.8346
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6488 - loss: 0.7343 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6495 - loss: 0.7402
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6503 - loss: 0.7440
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6508 - loss: 0.7459
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6515 - loss: 0.7466 - val_accuracy: 0.6252 - val_loss: 0.8146
Epoch 38/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8995
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6487 - loss: 0.7620 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6492 - loss: 0.7555
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6510 - loss: 0.7541
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6513 - loss: 0.7547
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6514 - loss: 0.7550 - val_accuracy: 0.6275 - val_loss: 0.8095

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 701ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 2: 60.26 [%]
F1-score capturado en la ejecución 2: 60.24 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:42[0m 852ms/step
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 775us/step  
[1m134/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 757us/step
[1m204/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 744us/step
[1m273/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 740us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m71/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 726us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.39 [%]
Global F1 score (validation) = 62.4 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44010076 0.55322164 0.00214832 0.00452924]
 [0.45121053 0.52091527 0.00651493 0.02135929]
 [0.46835765 0.46880138 0.01417715 0.04866381]
 ...
 [0.0359563  0.01440611 0.93263894 0.01699872]
 [0.03361406 0.0132922  0.93769705 0.01539674]
 [0.04868981 0.02017926 0.9050549  0.02607607]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.83 [%]
Global accuracy score (test) = 59.81 [%]
Global F1 score (train) = 66.78 [%]
Global F1 score (test) = 59.46 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.31      0.35       400
MODERATE-INTENSITY       0.49      0.59      0.54       400
         SEDENTARY       0.69      0.88      0.77       400
VIGOROUS-INTENSITY       0.88      0.61      0.72       345

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

2025-11-04 12:40:30.307243: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:40:30.318518: 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 00:00:1762256430.331965 1399203 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256430.336078 1399203 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256430.345894 1399203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256430.345914 1399203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256430.345916 1399203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256430.345917 1399203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:40:30.349091: 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 00:00:1762256432.718014 1399203 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256434.375567 1399335 service.cc:152] XLA service 0x7906bc0053a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256434.375602 1399335 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:40:34.416193: 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 00:00:1762256434.582049 1399335 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256436.795755 1399335 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 2/137

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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3837 - loss: 1.3727
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Epoch 3/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4211 - loss: 1.2468 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4258 - loss: 1.2351
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.2288
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4304 - loss: 1.2250
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4308 - loss: 1.2237 - val_accuracy: 0.5398 - val_loss: 1.0459
Epoch 4/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1537
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1520
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4709 - loss: 1.1511 - val_accuracy: 0.5391 - val_loss: 1.0220
Epoch 5/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.1190 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1163
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[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1111
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Epoch 6/137

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[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0693
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0682
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Epoch 7/137

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Epoch 8/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5515 - loss: 1.0292 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 1.0178
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5590 - loss: 1.0117
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5609 - loss: 1.0081
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Epoch 9/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5685 - loss: 1.0001 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5717 - loss: 0.9908
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[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5757 - loss: 0.9798
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Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 0.9325
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5657 - loss: 0.9378 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5727 - loss: 0.9420
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5750 - loss: 0.9427
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5759 - loss: 0.9431
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5761 - loss: 0.9432 - val_accuracy: 0.5936 - val_loss: 0.8595
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 0.9916
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5817 - loss: 0.9340 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5826 - loss: 0.9306
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5865 - loss: 0.9269
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5883 - loss: 0.9250
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5887 - loss: 0.9245 - val_accuracy: 0.6110 - val_loss: 0.8444
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0896
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5796 - loss: 0.9369 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5864 - loss: 0.9194
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5898 - loss: 0.9142
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5916 - loss: 0.9120
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5919 - loss: 0.9117 - val_accuracy: 0.6140 - val_loss: 0.8469
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8244
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5833 - loss: 0.9313 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5914 - loss: 0.9176
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[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5986 - loss: 0.9064
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Epoch 14/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6489 - loss: 0.8405 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6358 - loss: 0.8532
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6309 - loss: 0.8589
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6280 - loss: 0.8617
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6274 - loss: 0.8624 - val_accuracy: 0.6239 - val_loss: 0.8239
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.8728
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6167 - loss: 0.8639 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6179 - loss: 0.8648
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6164 - loss: 0.8665
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6161 - loss: 0.8659
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6160 - loss: 0.8659 - val_accuracy: 0.6097 - val_loss: 0.8282
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 0.8989
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5986 - loss: 0.8451 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6053 - loss: 0.8447
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6087 - loss: 0.8455
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6102 - loss: 0.8462
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6105 - loss: 0.8471 - val_accuracy: 0.6045 - val_loss: 0.8267
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8550
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6439 - loss: 0.8184 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6339 - loss: 0.8281
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6304 - loss: 0.8331
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6279 - loss: 0.8363
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6268 - loss: 0.8382 - val_accuracy: 0.6127 - val_loss: 0.8285
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.8172
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6260 - loss: 0.8197 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6293 - loss: 0.8205
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6310 - loss: 0.8213
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6313 - loss: 0.8233
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6312 - loss: 0.8246 - val_accuracy: 0.6051 - val_loss: 0.8240
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.7545
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6293 - loss: 0.8005 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6273 - loss: 0.8147
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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6269 - loss: 0.8207
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6263 - loss: 0.8227 - val_accuracy: 0.5995 - val_loss: 0.8279

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 823ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 3: 59.81 [%]
F1-score capturado en la ejecución 3: 59.46 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 689us/step
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 706us/step
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 765us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 60.28 [%]
Global F1 score (validation) = 60.58 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.42777437 0.32245794 0.17407048 0.07569725]
 [0.30543217 0.307629   0.02020629 0.3667325 ]
 [0.46192887 0.45618826 0.01337068 0.0685122 ]
 ...
 [0.03736668 0.01660637 0.93370247 0.01232448]
 [0.0369596  0.01637083 0.93372416 0.01294536]
 [0.11709709 0.06423791 0.78060734 0.03805763]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 65.83 [%]
Global accuracy score (test) = 56.96 [%]
Global F1 score (train) = 65.97 [%]
Global F1 score (test) = 57.57 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.44      0.39       400
MODERATE-INTENSITY       0.45      0.36      0.40       400
         SEDENTARY       0.70      0.86      0.77       400
VIGOROUS-INTENSITY       0.89      0.63      0.73       345

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

2025-11-04 12:40:57.841139: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:40:57.852370: 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 00:00:1762256457.865502 1401933 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256457.869598 1401933 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256457.879315 1401933 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256457.879329 1401933 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256457.879331 1401933 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256457.879332 1401933 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:40:57.882458: 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 00:00:1762256460.252967 1401933 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256461.961465 1402055 service.cc:152] XLA service 0x7b0ab000bb20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256461.961524 1402055 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:41:02.002260: 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 00:00:1762256462.170093 1402055 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256464.367848 1402055 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:31[0m 3s/step - accuracy: 0.2500 - loss: 2.2382
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2832 - loss: 2.0380 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2966 - loss: 1.9501
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3023 - loss: 1.8889
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3069 - loss: 1.8444
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3093 - loss: 1.8223
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3094 - loss: 1.8212 - val_accuracy: 0.4747 - val_loss: 1.1326
Epoch 2/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3822 - loss: 1.3559 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3814 - loss: 1.3499
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3831 - loss: 1.3449
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3843 - loss: 1.3388
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3861 - loss: 1.3335 - val_accuracy: 0.5043 - val_loss: 1.0963
Epoch 3/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4124 - loss: 1.2594 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4189 - loss: 1.2415
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4248 - loss: 1.2294
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4296 - loss: 1.2198
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Epoch 4/137

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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1513
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Epoch 5/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1128
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Epoch 6/137

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[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0883
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0850
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0811
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Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0432
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0406 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0435
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0435
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5372 - loss: 1.0415
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5376 - loss: 1.0401 - val_accuracy: 0.5907 - val_loss: 0.9263
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0299
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5739 - loss: 1.0008 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5704 - loss: 1.0053
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5663 - loss: 1.0068
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5633 - loss: 1.0069
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5625 - loss: 1.0062 - val_accuracy: 0.5867 - val_loss: 0.9255
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 0.9837
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5682 - loss: 0.9812 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5701 - loss: 0.9776
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5713 - loss: 0.9760
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5717 - loss: 0.9754
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5717 - loss: 0.9754 - val_accuracy: 0.6018 - val_loss: 0.8833
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0454
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5867 - loss: 0.9479 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5802 - loss: 0.9551
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5787 - loss: 0.9581
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5799 - loss: 0.9581
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5802 - loss: 0.9581 - val_accuracy: 0.6022 - val_loss: 0.8756
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9208
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5964 - loss: 0.9236 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5987 - loss: 0.9228
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5981 - loss: 0.9243
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5968 - loss: 0.9256
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Epoch 12/137

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Epoch 13/137

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Epoch 14/137

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[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6031 - loss: 0.9118
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Epoch 15/137

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[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6134 - loss: 0.8765
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Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9277
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6110 - loss: 0.8634 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6115 - loss: 0.8624
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6116 - loss: 0.8644
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Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.9360
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6185 - loss: 0.8353 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6099 - loss: 0.8500
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6070 - loss: 0.8560
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6062 - loss: 0.8569
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6062 - loss: 0.8567 - val_accuracy: 0.6271 - val_loss: 0.8241
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.8866
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6340 - loss: 0.8151 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6345 - loss: 0.8200
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6331 - loss: 0.8254
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6318 - loss: 0.8290
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6310 - loss: 0.8310 - val_accuracy: 0.6196 - val_loss: 0.8265
Epoch 19/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6343 - loss: 0.8342 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6282 - loss: 0.8379
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Epoch 20/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6230 - loss: 0.8126 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6241 - loss: 0.8257
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6269 - loss: 0.8315 - val_accuracy: 0.6064 - val_loss: 0.8252
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7500 - loss: 0.7282
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6241 - loss: 0.8744 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6211 - loss: 0.8680
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6228 - loss: 0.8605
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6244 - loss: 0.8530
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6251 - loss: 0.8507 - val_accuracy: 0.6245 - val_loss: 0.8205
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.7344 - loss: 0.6001
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6490 - loss: 0.8034 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6413 - loss: 0.8132
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6396 - loss: 0.8134
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6385 - loss: 0.8139
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6380 - loss: 0.8147 - val_accuracy: 0.6229 - val_loss: 0.8215
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.8396
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6219 - loss: 0.8308 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6284 - loss: 0.8231
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6304 - loss: 0.8221
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Epoch 24/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6096 - loss: 0.8213 
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[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6249 - loss: 0.8161
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6249 - loss: 0.8163 - val_accuracy: 0.6193 - val_loss: 0.8146

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 831ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 4: 56.96 [%]
F1-score capturado en la ejecución 4: 57.57 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:32[0m 821ms/step
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[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 747us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.73 [%]
Global F1 score (validation) = 61.86 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.27269706 0.15040943 0.46213424 0.11475925]
 [0.45728838 0.48235172 0.0153406  0.04501919]
 [0.4725028  0.4851482  0.01755194 0.02479714]
 ...
 [0.02812874 0.01272185 0.9512404  0.00790901]
 [0.01746805 0.00714277 0.9689267  0.00646241]
 [0.01366918 0.00518291 0.9754403  0.00570771]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.23 [%]
Global accuracy score (test) = 59.22 [%]
Global F1 score (train) = 66.08 [%]
Global F1 score (test) = 58.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.28      0.33       400
MODERATE-INTENSITY       0.47      0.56      0.51       400
         SEDENTARY       0.67      0.88      0.76       400
VIGOROUS-INTENSITY       0.90      0.65      0.76       345

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

2025-11-04 12:41:27.254628: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:41:27.265985: 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 00:00:1762256487.279366 1405126 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256487.283557 1405126 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256487.293510 1405126 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256487.293528 1405126 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256487.293529 1405126 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256487.293530 1405126 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:41:27.296654: 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 00:00:1762256489.675885 1405126 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256491.342922 1405223 service.cc:152] XLA service 0x74560800c450 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256491.342974 1405223 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:41:31.379214: 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 00:00:1762256491.543688 1405223 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256493.794428 1405223 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:28[0m 3s/step - accuracy: 0.2344 - loss: 2.2934
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2639 - loss: 2.1348 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2829 - loss: 2.0327
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2926 - loss: 1.9699
[1m141/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2986 - loss: 1.9200
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3025 - loss: 1.8874
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3027 - loss: 1.8863 - val_accuracy: 0.4478 - val_loss: 1.1553
Epoch 2/137

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[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3756 - loss: 1.3865 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3771 - loss: 1.3727
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3787 - loss: 1.3641
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3799 - loss: 1.3550
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3807 - loss: 1.3501 - val_accuracy: 0.5145 - val_loss: 1.0921
Epoch 3/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4374 - loss: 1.2085 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.2144
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4323 - loss: 1.2162
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4318 - loss: 1.2153
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4320 - loss: 1.2148 - val_accuracy: 0.5434 - val_loss: 1.0380
Epoch 4/137

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[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4572 - loss: 1.1834
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1797
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1756
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Epoch 5/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1174 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1126
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1103
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1090
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Epoch 6/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1041 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.1005
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[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0927
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Epoch 7/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0535 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.0586
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0566
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0550
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5299 - loss: 1.0543 - val_accuracy: 0.5871 - val_loss: 0.9339
Epoch 8/137

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[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0253
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0256
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0238
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5402 - loss: 1.0233 - val_accuracy: 0.5907 - val_loss: 0.9022
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9962
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5413 - loss: 1.0084 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0101
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0083
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0050
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5498 - loss: 1.0037 - val_accuracy: 0.6038 - val_loss: 0.8841
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0179
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5629 - loss: 0.9798 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5649 - loss: 0.9726
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5672 - loss: 0.9706
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5687 - loss: 0.9692
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5692 - loss: 0.9686 - val_accuracy: 0.6140 - val_loss: 0.8551
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9907
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5758 - loss: 0.9563 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5800 - loss: 0.9464
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5816 - loss: 0.9427
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5826 - loss: 0.9411
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5828 - loss: 0.9410 - val_accuracy: 0.6078 - val_loss: 0.8594
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0449
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5855 - loss: 0.9355 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5840 - loss: 0.9287
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5847 - loss: 0.9261
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5859 - loss: 0.9251
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5866 - loss: 0.9249 - val_accuracy: 0.6235 - val_loss: 0.8508
Epoch 13/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6147 - loss: 0.8452 
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Epoch 14/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6170 - loss: 0.8728 
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[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6106 - loss: 0.8782
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Epoch 15/137

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[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6106 - loss: 0.8950
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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6066 - loss: 0.8945
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Epoch 16/137

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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6162 - loss: 0.8655
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6152 - loss: 0.8675 - val_accuracy: 0.6025 - val_loss: 0.8352
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.7612
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6076 - loss: 0.8323 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6149 - loss: 0.8394
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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6154 - loss: 0.8520
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6157 - loss: 0.8530 - val_accuracy: 0.6206 - val_loss: 0.8321
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9299
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6159 - loss: 0.8946 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6185 - loss: 0.8769
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6193 - loss: 0.8694
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6190 - loss: 0.8669
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6191 - loss: 0.8658 - val_accuracy: 0.6183 - val_loss: 0.8338
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8558
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6202 - loss: 0.8435 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6176 - loss: 0.8500
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6181 - loss: 0.8505
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6190 - loss: 0.8498
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6192 - loss: 0.8499 - val_accuracy: 0.6055 - val_loss: 0.8280
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.8697
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6077 - loss: 0.8461 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6103 - loss: 0.8488
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6131 - loss: 0.8487
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6150 - loss: 0.8484
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Epoch 21/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6139 - loss: 0.8201 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6178 - loss: 0.8222
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[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6210 - loss: 0.8281
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Epoch 22/137

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[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6332 - loss: 0.8195 
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Epoch 23/137

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[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6337 - loss: 0.8294
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Epoch 24/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6389 - loss: 0.7932 
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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6367 - loss: 0.8006
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6351 - loss: 0.8040
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Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8429
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6457 - loss: 0.8252 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6415 - loss: 0.8214
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6401 - loss: 0.8188
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.8166
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6395 - loss: 0.8154 - val_accuracy: 0.6097 - val_loss: 0.8165
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.7624
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6525 - loss: 0.7867 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6498 - loss: 0.7911
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6463 - loss: 0.7932
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6440 - loss: 0.7950
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6436 - loss: 0.7954 - val_accuracy: 0.6091 - val_loss: 0.8278
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.8538
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6436 - loss: 0.7877 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6443 - loss: 0.7911
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6431 - loss: 0.7939
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6425 - loss: 0.7944
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6420 - loss: 0.7948 - val_accuracy: 0.6173 - val_loss: 0.8075
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.8103
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6300 - loss: 0.8001 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6341 - loss: 0.7967
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6360 - loss: 0.7958
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6369 - loss: 0.7964
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6371 - loss: 0.7968 - val_accuracy: 0.6183 - val_loss: 0.8175
Epoch 29/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7225
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6336 - loss: 0.8040 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6356 - loss: 0.7963
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6371 - loss: 0.7953
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6380 - loss: 0.7947
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6382 - loss: 0.7942 - val_accuracy: 0.6133 - val_loss: 0.8158
Epoch 30/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6298 - loss: 0.7798 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6365 - loss: 0.7805
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6394 - loss: 0.7806
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6396 - loss: 0.7840
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6396 - loss: 0.7847 - val_accuracy: 0.6166 - val_loss: 0.8318
Epoch 31/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.7656 - loss: 0.6636
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6701 - loss: 0.7896 
[1m 67/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6598 - loss: 0.7839
[1m102/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6560 - loss: 0.7820
[1m140/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6528 - loss: 0.7836
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6513 - loss: 0.7840 - val_accuracy: 0.6166 - val_loss: 0.7992
Epoch 32/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8283
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6382 - loss: 0.8081 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6422 - loss: 0.8039
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6455 - loss: 0.7987
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6468 - loss: 0.7950
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6469 - loss: 0.7942 - val_accuracy: 0.6133 - val_loss: 0.8107
Epoch 33/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7075
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6350 - loss: 0.7502 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6414 - loss: 0.7530
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6437 - loss: 0.7559
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6445 - loss: 0.7591
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6446 - loss: 0.7607 - val_accuracy: 0.6183 - val_loss: 0.8070
Epoch 34/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.7792
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6346 - loss: 0.7774 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6450 - loss: 0.7700
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6461 - loss: 0.7711
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6458 - loss: 0.7717
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6459 - loss: 0.7716 - val_accuracy: 0.6183 - val_loss: 0.8108
Epoch 35/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7094
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6532 - loss: 0.7695 
[1m 69/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6536 - loss: 0.7683
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6535 - loss: 0.7695
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6530 - loss: 0.7701
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6522 - loss: 0.7713 - val_accuracy: 0.6127 - val_loss: 0.8263
Epoch 36/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.7281
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6507 - loss: 0.7676 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6549 - loss: 0.7648
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[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6583 - loss: 0.7619
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6586 - loss: 0.7622 - val_accuracy: 0.6242 - val_loss: 0.8184

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 700ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Saved model to disk.

Accuracy capturado en la ejecución 5: 59.22 [%]
F1-score capturado en la ejecución 5: 58.91 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 788us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.88 [%]
Global F1 score (validation) = 62.02 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.46347556 0.50253797 0.01392163 0.0200649 ]
 [0.48597777 0.38604128 0.09869418 0.02928672]
 [0.4486357  0.49066114 0.02305648 0.03764668]
 ...
 [0.03141895 0.01177788 0.944057   0.01274619]
 [0.04366327 0.01721961 0.9188611  0.0202561 ]
 [0.04848798 0.01970019 0.91024566 0.02156616]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.68 [%]
Global accuracy score (test) = 60.0 [%]
Global F1 score (train) = 66.77 [%]
Global F1 score (test) = 59.84 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.29      0.33       400
MODERATE-INTENSITY       0.47      0.59      0.53       400
         SEDENTARY       0.70      0.88      0.78       400
VIGOROUS-INTENSITY       0.92      0.64      0.75       345

          accuracy                           0.60      1545
         macro avg       0.62      0.60      0.60      1545
      weighted avg       0.61      0.60      0.59      1545

2025-11-04 12:42:01.089241: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:42:01.100527: 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 00:00:1762256521.114189 1409394 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256521.118364 1409394 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256521.128252 1409394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256521.128270 1409394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256521.128272 1409394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256521.128273 1409394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:42:01.131415: 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 00:00:1762256523.507953 1409394 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256525.197539 1409529 service.cc:152] XLA service 0x7c2f1000d0d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256525.197580 1409529 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:42:05.248005: 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 00:00:1762256525.429767 1409529 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256527.680784 1409529 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:40[0m 3s/step - accuracy: 0.2969 - loss: 1.9316
[1m 28/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3065 - loss: 1.9598 
[1m 63/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3051 - loss: 1.9099
[1m101/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3086 - loss: 1.8568
[1m137/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3124 - loss: 1.8140
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3156 - loss: 1.7821
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3157 - loss: 1.7811 - val_accuracy: 0.4665 - val_loss: 1.1358
Epoch 2/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.4531 - loss: 1.2141
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3946 - loss: 1.3147 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3985 - loss: 1.3123
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3990 - loss: 1.3075
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3988 - loss: 1.3035
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Epoch 3/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4397 - loss: 1.1917 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4356 - loss: 1.1942
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4351 - loss: 1.1950
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Epoch 4/137

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[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4408 - loss: 1.1598 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4501 - loss: 1.1557
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4578 - loss: 1.1488
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4622 - loss: 1.1451
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Epoch 5/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.0987 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.0893
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.0875
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.0865
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5073 - loss: 1.0862 - val_accuracy: 0.5733 - val_loss: 0.9511
Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1073
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0457 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5334 - loss: 1.0378
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0374
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0389
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5305 - loss: 1.0397 - val_accuracy: 0.5897 - val_loss: 0.9198
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.4062 - loss: 1.2821
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0572 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0414
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0314
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0268
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5415 - loss: 1.0261 - val_accuracy: 0.5956 - val_loss: 0.8941
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8925
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5804 - loss: 0.9660 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5723 - loss: 0.9696
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5706 - loss: 0.9714
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5700 - loss: 0.9713
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5698 - loss: 0.9711 - val_accuracy: 0.6035 - val_loss: 0.8697
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1604
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5576 - loss: 0.9988 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5624 - loss: 0.9836
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5656 - loss: 0.9761
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5675 - loss: 0.9710
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5683 - loss: 0.9691 - val_accuracy: 0.6048 - val_loss: 0.8467
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 1.0270
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5956 - loss: 0.9453 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5916 - loss: 0.9356
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5886 - loss: 0.9337
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5873 - loss: 0.9327
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5869 - loss: 0.9326 - val_accuracy: 0.6045 - val_loss: 0.8382
Epoch 11/137

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[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6005 - loss: 0.8992
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Epoch 12/137

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Epoch 13/137

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[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6196 - loss: 0.8768
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6135 - loss: 0.8810
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Epoch 14/137

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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6032 - loss: 0.8730
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Epoch 15/137

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[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6376 - loss: 0.8508 
[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6323 - loss: 0.8582
[1m105/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6271 - loss: 0.8612
[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6242 - loss: 0.8623
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Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8172
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6407 - loss: 0.8225 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6325 - loss: 0.8348
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6271 - loss: 0.8425
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6242 - loss: 0.8467
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6237 - loss: 0.8477 - val_accuracy: 0.6406 - val_loss: 0.7922
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6094 - loss: 0.7733
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6360 - loss: 0.8398 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6292 - loss: 0.8480
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6254 - loss: 0.8500
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6227 - loss: 0.8512
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6220 - loss: 0.8512 - val_accuracy: 0.6357 - val_loss: 0.7974
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.6094 - loss: 0.9841
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6243 - loss: 0.8610 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6263 - loss: 0.8465
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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6261 - loss: 0.8409
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Epoch 19/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6305 - loss: 0.8149 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6286 - loss: 0.8219
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6282 - loss: 0.8246 - val_accuracy: 0.6153 - val_loss: 0.8124
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.8928
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6467 - loss: 0.8006 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6408 - loss: 0.8035
[1m103/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6369 - loss: 0.8090
[1m140/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6339 - loss: 0.8145
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6332 - loss: 0.8161 - val_accuracy: 0.6268 - val_loss: 0.8178
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.6406 - loss: 0.7298
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6297 - loss: 0.8210 
[1m 69/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6289 - loss: 0.8217
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6282 - loss: 0.8199
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6281 - loss: 0.8206
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6280 - loss: 0.8210 - val_accuracy: 0.6360 - val_loss: 0.7898
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8381
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6402 - loss: 0.8263 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6420 - loss: 0.8099
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6412 - loss: 0.8066
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6411 - loss: 0.8050
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6410 - loss: 0.8048 - val_accuracy: 0.6212 - val_loss: 0.7916
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9210
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6308 - loss: 0.8056 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6287 - loss: 0.8076
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6285 - loss: 0.8084
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6291 - loss: 0.8089
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6292 - loss: 0.8092 - val_accuracy: 0.6179 - val_loss: 0.8036
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6719 - loss: 0.7107
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6374 - loss: 0.7864 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6394 - loss: 0.7900
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6383 - loss: 0.7911
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6375 - loss: 0.7913
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6372 - loss: 0.7915 - val_accuracy: 0.6216 - val_loss: 0.8005
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7031 - loss: 0.6380
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6531 - loss: 0.7765 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6500 - loss: 0.7803
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6467 - loss: 0.7847
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6456 - loss: 0.7859
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6449 - loss: 0.7870 - val_accuracy: 0.6160 - val_loss: 0.8003
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.8829
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6304 - loss: 0.7964 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6392 - loss: 0.7912
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6408 - loss: 0.7884
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6413 - loss: 0.7869
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6413 - loss: 0.7869 - val_accuracy: 0.6347 - val_loss: 0.7965

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 846ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 6: 60.0 [%]
F1-score capturado en la ejecución 6: 59.84 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:45[0m 859ms/step
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[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 715us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 812us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.55 [%]
Global F1 score (validation) = 61.64 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.38566598 0.49486536 0.00380054 0.11566816]
 [0.3984166  0.430557   0.01478186 0.15624452]
 [0.4344631  0.5007442  0.01426893 0.05052377]
 ...
 [0.02678422 0.01036774 0.9513584  0.0114897 ]
 [0.03320933 0.01377068 0.9392565  0.01376352]
 [0.0325358  0.01307659 0.93986803 0.01451955]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.5 [%]
Global accuracy score (test) = 58.77 [%]
Global F1 score (train) = 65.66 [%]
Global F1 score (test) = 58.42 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.28      0.32       400
MODERATE-INTENSITY       0.47      0.58      0.52       400
         SEDENTARY       0.68      0.86      0.76       400
VIGOROUS-INTENSITY       0.87      0.63      0.73       345

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

2025-11-04 12:42:31.406646: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:42:31.418138: 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 00:00:1762256551.431482 1412762 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256551.435490 1412762 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256551.445762 1412762 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256551.445778 1412762 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256551.445780 1412762 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256551.445781 1412762 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:42:31.448981: 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 00:00:1762256553.834128 1412762 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256555.508710 1412901 service.cc:152] XLA service 0x7c2d1c00c440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256555.508736 1412901 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:42:35.542328: 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 00:00:1762256555.714809 1412901 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256557.926509 1412901 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:26[0m 3s/step - accuracy: 0.2812 - loss: 1.9577
[1m 30/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2636 - loss: 2.0350 
[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2713 - loss: 1.9763
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2801 - loss: 1.9170
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2878 - loss: 1.8655
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2913 - loss: 1.8424
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2914 - loss: 1.8413 - val_accuracy: 0.4511 - val_loss: 1.1507
Epoch 2/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3653 - loss: 1.3741 
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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3721 - loss: 1.3568
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3756 - loss: 1.3505
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Epoch 3/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4226 - loss: 1.2344 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4230 - loss: 1.2322
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Epoch 4/137

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[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4374 - loss: 1.1895
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4452 - loss: 1.1794
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Epoch 5/137

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[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4808 - loss: 1.1452 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1330
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1281
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1239
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Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0749
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0820 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5288 - loss: 1.0850
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0836
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0821
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5251 - loss: 1.0816 - val_accuracy: 0.5578 - val_loss: 0.9516
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1329
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0751 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0708
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0661
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0620
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5270 - loss: 1.0605 - val_accuracy: 0.5782 - val_loss: 0.9118
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6094 - loss: 0.9388
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5681 - loss: 0.9840 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5617 - loss: 0.9976
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 1.0031
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5578 - loss: 1.0049
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5572 - loss: 1.0055 - val_accuracy: 0.5772 - val_loss: 0.9020
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9442
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5620 - loss: 1.0069 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5702 - loss: 0.9903
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5727 - loss: 0.9850
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5731 - loss: 0.9830
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5732 - loss: 0.9821 - val_accuracy: 0.5933 - val_loss: 0.8790
Epoch 10/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5810 - loss: 0.9496 
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Epoch 11/137

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Epoch 12/137

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[1m 69/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6059 - loss: 0.9103
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6000 - loss: 0.9129
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5977 - loss: 0.9144
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Epoch 13/137

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[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6114 - loss: 0.9010
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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6096 - loss: 0.9014
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Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.5938 - loss: 0.8186
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6016 - loss: 0.9012 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6037 - loss: 0.8897
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6041 - loss: 0.8880
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6032 - loss: 0.8888
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6032 - loss: 0.8885 - val_accuracy: 0.6081 - val_loss: 0.8354
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 0.9213
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6094 - loss: 0.8767 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6115 - loss: 0.8755
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6121 - loss: 0.8768
[1m141/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6113 - loss: 0.8793
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6111 - loss: 0.8802 - val_accuracy: 0.6186 - val_loss: 0.8217
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7785
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6261 - loss: 0.8626 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6187 - loss: 0.8738
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6172 - loss: 0.8752
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6168 - loss: 0.8748
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6162 - loss: 0.8750 - val_accuracy: 0.6110 - val_loss: 0.8270
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.8350
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6184 - loss: 0.8279 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6189 - loss: 0.8365
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6173 - loss: 0.8429
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6156 - loss: 0.8483
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6151 - loss: 0.8498 - val_accuracy: 0.6265 - val_loss: 0.8194
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.9229
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6240 - loss: 0.8566 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6298 - loss: 0.8422
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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6302 - loss: 0.8418
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Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.7072
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6305 - loss: 0.8185 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6260 - loss: 0.8290
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6248 - loss: 0.8327
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6246 - loss: 0.8338
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6242 - loss: 0.8348 - val_accuracy: 0.6117 - val_loss: 0.8225
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.8352
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6175 - loss: 0.8728 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6172 - loss: 0.8743
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6195 - loss: 0.8700
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6215 - loss: 0.8658
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6219 - loss: 0.8641 - val_accuracy: 0.6209 - val_loss: 0.8221
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.8717
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6320 - loss: 0.7928 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6306 - loss: 0.8036
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6303 - loss: 0.8078
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6301 - loss: 0.8115
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6299 - loss: 0.8135 - val_accuracy: 0.6317 - val_loss: 0.8108
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.6882
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6337 - loss: 0.8015 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6369 - loss: 0.7999
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6367 - loss: 0.8026
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6369 - loss: 0.8045
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6366 - loss: 0.8056 - val_accuracy: 0.6150 - val_loss: 0.8176
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7764
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6313 - loss: 0.8123 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6386 - loss: 0.8099
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6390 - loss: 0.8113
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6391 - loss: 0.8116
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6391 - loss: 0.8116 - val_accuracy: 0.6193 - val_loss: 0.8265
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6562 - loss: 0.8182
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6261 - loss: 0.8540 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6302 - loss: 0.8393
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6315 - loss: 0.8328
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6331 - loss: 0.8285
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6337 - loss: 0.8267 - val_accuracy: 0.6219 - val_loss: 0.8179
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 0.8266
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6210 - loss: 0.8375 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6289 - loss: 0.8210
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6320 - loss: 0.8164
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6333 - loss: 0.8147
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6341 - loss: 0.8134 - val_accuracy: 0.6370 - val_loss: 0.8162
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7847
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6370 - loss: 0.7926 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6343 - loss: 0.8000
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6340 - loss: 0.8005
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6347 - loss: 0.8000
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6352 - loss: 0.7998 - val_accuracy: 0.6163 - val_loss: 0.8110

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 804ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 7: 58.77 [%]
F1-score capturado en la ejecución 7: 58.42 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:42[0m 850ms/step
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 844us/step  
[1m131/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 775us/step
[1m198/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 765us/step
[1m270/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 748us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 748us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.7 [%]
Global F1 score (validation) = 61.78 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44948122 0.47205466 0.02004908 0.05841501]
 [0.48588857 0.40023676 0.05827313 0.05560144]
 [0.47097075 0.4013588  0.06242096 0.06524947]
 ...
 [0.03087379 0.01257189 0.9437344  0.01281991]
 [0.02892699 0.01159172 0.94750935 0.011972  ]
 [0.03355906 0.01384515 0.93869114 0.01390463]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.59 [%]
Global accuracy score (test) = 59.16 [%]
Global F1 score (train) = 66.68 [%]
Global F1 score (test) = 59.19 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.34      0.36       400
MODERATE-INTENSITY       0.48      0.53      0.50       400
         SEDENTARY       0.68      0.88      0.76       400
VIGOROUS-INTENSITY       0.89      0.63      0.74       345

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

2025-11-04 12:43:01.683096: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:43:01.694816: 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 00:00:1762256581.708075 1416136 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256581.712034 1416136 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256581.722094 1416136 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256581.722111 1416136 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256581.722113 1416136 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256581.722115 1416136 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:43:01.725215: 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 00:00:1762256584.068599 1416136 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256585.716833 1416268 service.cc:152] XLA service 0x7c161000c340 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256585.716859 1416268 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:43:05.749096: 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 00:00:1762256585.916872 1416268 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256588.137635 1416268 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:26[0m 3s/step - accuracy: 0.1875 - loss: 2.1784
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2713 - loss: 1.9771 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2867 - loss: 1.9006
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2966 - loss: 1.8412
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3035 - loss: 1.7951
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3059 - loss: 1.7769 - val_accuracy: 0.4537 - val_loss: 1.1554
Epoch 2/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.2969 - loss: 1.3925
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3771 - loss: 1.3580 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3817 - loss: 1.3486
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3843 - loss: 1.3399
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3856 - loss: 1.3320
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Epoch 3/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3406
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3968 - loss: 1.2432 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4058 - loss: 1.2361
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4100 - loss: 1.2310
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4137 - loss: 1.2255
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Epoch 4/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1532
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4583 - loss: 1.1589 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4618 - loss: 1.1540
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1528
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4676 - loss: 1.1495
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4691 - loss: 1.1477 - val_accuracy: 0.5375 - val_loss: 1.0297
Epoch 5/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2029
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1491 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1363
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1328
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1290
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4758 - loss: 1.1279 - val_accuracy: 0.5493 - val_loss: 1.0079
Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6094 - loss: 0.9786
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0447 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0552
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0606
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0621
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5250 - loss: 1.0624 - val_accuracy: 0.5670 - val_loss: 0.9734
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.1342
[1m 30/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5259 - loss: 1.0535 
[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5340 - loss: 1.0423
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0358
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0332
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5405 - loss: 1.0319 - val_accuracy: 0.5907 - val_loss: 0.9315
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0449
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0114 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5481 - loss: 1.0120
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5516 - loss: 1.0094
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 1.0085
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5530 - loss: 1.0080 - val_accuracy: 0.5818 - val_loss: 0.9098
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9829
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5600 - loss: 0.9743 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5627 - loss: 0.9721
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5635 - loss: 0.9726
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5651 - loss: 0.9720
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Epoch 10/137

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[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5765 - loss: 0.9529
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5787 - loss: 0.9508
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Epoch 11/137

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[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5737 - loss: 0.9363
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5798 - loss: 0.9339
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5826 - loss: 0.9321
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Epoch 12/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5671 - loss: 0.9418 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5756 - loss: 0.9334
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5799 - loss: 0.9278
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5822 - loss: 0.9238
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5829 - loss: 0.9224 - val_accuracy: 0.6133 - val_loss: 0.8510
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8968
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5963 - loss: 0.8972 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5973 - loss: 0.8952
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5994 - loss: 0.8915
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6010 - loss: 0.8902
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6013 - loss: 0.8902 - val_accuracy: 0.6143 - val_loss: 0.8515
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9301
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5997 - loss: 0.9298 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6000 - loss: 0.9156
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6019 - loss: 0.9063
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6034 - loss: 0.9022
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6040 - loss: 0.9003 - val_accuracy: 0.6048 - val_loss: 0.8411
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8542
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6061 - loss: 0.8848 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6094 - loss: 0.8785
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6107 - loss: 0.8733
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6106 - loss: 0.8721
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6107 - loss: 0.8717 - val_accuracy: 0.6032 - val_loss: 0.8585
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7500 - loss: 0.7614
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6346 - loss: 0.8379 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6276 - loss: 0.8497
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6250 - loss: 0.8549
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6229 - loss: 0.8572
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6227 - loss: 0.8574 - val_accuracy: 0.6061 - val_loss: 0.8322
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.7446
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6445 - loss: 0.8186 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6338 - loss: 0.8357
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6300 - loss: 0.8414
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6273 - loss: 0.8454
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Epoch 18/137

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Epoch 19/137

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Epoch 20/137

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[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6426 - loss: 0.8238
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Epoch 21/137

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Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6250 - loss: 0.6716
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6452 - loss: 0.7949 
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[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6384 - loss: 0.8026
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6382 - loss: 0.8039
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6384 - loss: 0.8044 - val_accuracy: 0.6114 - val_loss: 0.8144
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8250
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6310 - loss: 0.8223 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6331 - loss: 0.8277
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6349 - loss: 0.8233
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6354 - loss: 0.8207
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6354 - loss: 0.8202 - val_accuracy: 0.6199 - val_loss: 0.8201
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9043
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6533 - loss: 0.7854 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6503 - loss: 0.7917
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6482 - loss: 0.7964
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6466 - loss: 0.7990
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6463 - loss: 0.7993 - val_accuracy: 0.6206 - val_loss: 0.8143
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.8734
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6384 - loss: 0.7870 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6379 - loss: 0.7933
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[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6399 - loss: 0.7966
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Epoch 26/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6424 - loss: 0.8034 
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6424 - loss: 0.7942
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6413 - loss: 0.7948
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6410 - loss: 0.7949 - val_accuracy: 0.6176 - val_loss: 0.8226
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6719 - loss: 0.8182
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6487 - loss: 0.8004 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6479 - loss: 0.7963
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6484 - loss: 0.7955
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6478 - loss: 0.7948
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6475 - loss: 0.7946 - val_accuracy: 0.6245 - val_loss: 0.8225
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.6461
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6409 - loss: 0.7804 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6393 - loss: 0.7844
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6413 - loss: 0.7834
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6426 - loss: 0.7828
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6428 - loss: 0.7828 - val_accuracy: 0.6140 - val_loss: 0.8176
Epoch 29/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7344 - loss: 0.7108
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6536 - loss: 0.7990 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6542 - loss: 0.7910
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6549 - loss: 0.7868
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6547 - loss: 0.7860
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6547 - loss: 0.7857 - val_accuracy: 0.6150 - val_loss: 0.8292

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 671ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 8: 59.16 [%]
F1-score capturado en la ejecución 8: 59.19 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:44[0m 856ms/step
[1m 62/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 827us/step  
[1m131/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 775us/step
[1m203/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 748us/step
[1m277/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 730us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 750us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 61.83 [%]
Global F1 score (validation) = 62.26 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.48454377 0.45443144 0.023076   0.03794875]
 [0.45542374 0.5367243  0.00287745 0.0049745 ]
 [0.4649655  0.5186078  0.00396403 0.01246271]
 ...
 [0.03408368 0.01464828 0.938568   0.01270007]
 [0.0681608  0.03292114 0.87078017 0.0281378 ]
 [0.03447039 0.0148599  0.9378215  0.01284819]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.38 [%]
Global accuracy score (test) = 59.61 [%]
Global F1 score (train) = 67.21 [%]
Global F1 score (test) = 60.79 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.40      0.39       400
MODERATE-INTENSITY       0.46      0.52      0.49       400
         SEDENTARY       0.78      0.82      0.80       400
VIGOROUS-INTENSITY       0.90      0.65      0.75       345

          accuracy                           0.60      1545
         macro avg       0.63      0.60      0.61      1545
      weighted avg       0.62      0.60      0.60      1545

2025-11-04 12:43:32.678142: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:43:32.689476: 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 00:00:1762256612.702720 1419792 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256612.706878 1419792 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256612.716578 1419792 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256612.716593 1419792 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256612.716595 1419792 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256612.716596 1419792 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:43:32.719728: 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 00:00:1762256615.059268 1419792 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256616.725183 1419910 service.cc:152] XLA service 0x79916c003fd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256616.725214 1419910 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:43:36.760081: 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 00:00:1762256616.933215 1419910 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256619.196073 1419910 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 2/137

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[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3876 - loss: 1.3501
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Epoch 3/137

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[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4190 - loss: 1.2316
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4223 - loss: 1.2260
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4229 - loss: 1.2246 - val_accuracy: 0.4984 - val_loss: 1.0656
Epoch 4/137

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[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1550
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1517
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4638 - loss: 1.1509 - val_accuracy: 0.5269 - val_loss: 1.0315
Epoch 5/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1238 
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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1199
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1178
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Epoch 6/137

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Epoch 7/137

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[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 1.0149 
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Epoch 8/137

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[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0136
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Epoch 9/137

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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5709 - loss: 0.9736
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5706 - loss: 0.9738
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Epoch 10/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5598 - loss: 0.9792 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5666 - loss: 0.9706
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5682 - loss: 0.9675
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5690 - loss: 0.9655
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5692 - loss: 0.9651 - val_accuracy: 0.6002 - val_loss: 0.8779
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.1091
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5898 - loss: 0.9737 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5916 - loss: 0.9552
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5905 - loss: 0.9505
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5895 - loss: 0.9486
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5885 - loss: 0.9473 - val_accuracy: 0.6038 - val_loss: 0.8510
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9162
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6100 - loss: 0.9111 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6055 - loss: 0.9112
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6025 - loss: 0.9123
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6012 - loss: 0.9128
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6009 - loss: 0.9131 - val_accuracy: 0.6015 - val_loss: 0.8506
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6250 - loss: 0.7833
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6010 - loss: 0.9004 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5998 - loss: 0.9019
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5996 - loss: 0.9023
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Epoch 14/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5976 - loss: 0.8948 
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[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6016 - loss: 0.8865
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Epoch 15/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6097 - loss: 0.8919 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6104 - loss: 0.8843
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6108 - loss: 0.8801
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Epoch 16/137

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[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6014 - loss: 0.8525
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6050 - loss: 0.8573
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6065 - loss: 0.8594
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Epoch 17/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6225 - loss: 0.8633 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6198 - loss: 0.8629
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6193 - loss: 0.8617
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6189 - loss: 0.8591
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6184 - loss: 0.8577 - val_accuracy: 0.6147 - val_loss: 0.8144
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.6094 - loss: 0.8276
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6232 - loss: 0.8402 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6270 - loss: 0.8470
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6282 - loss: 0.8479
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6273 - loss: 0.8493
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6272 - loss: 0.8494 - val_accuracy: 0.6051 - val_loss: 0.8275
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7694
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6307 - loss: 0.8377 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6279 - loss: 0.8425
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6255 - loss: 0.8439
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6248 - loss: 0.8433
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6248 - loss: 0.8428 - val_accuracy: 0.6032 - val_loss: 0.8225
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.7219
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6355 - loss: 0.7681 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6307 - loss: 0.7891
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6285 - loss: 0.7995
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6266 - loss: 0.8067
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6262 - loss: 0.8084 - val_accuracy: 0.6078 - val_loss: 0.8248
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7188 - loss: 0.8622
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6498 - loss: 0.8215 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6415 - loss: 0.8259
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6381 - loss: 0.8253
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6368 - loss: 0.8245
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6362 - loss: 0.8246 - val_accuracy: 0.6160 - val_loss: 0.8176
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6562 - loss: 0.8169
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6382 - loss: 0.8092 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6360 - loss: 0.8179
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6342 - loss: 0.8215
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6331 - loss: 0.8219
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 832ms/step
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Saved model to disk.

Accuracy capturado en la ejecución 9: 59.61 [%]
F1-score capturado en la ejecución 9: 60.79 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:42[0m 852ms/step
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[1m128/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 792us/step
[1m196/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 775us/step
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 759us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 782us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 62.29 [%]
Global F1 score (validation) = 62.6 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.47910598 0.39824596 0.06190667 0.06074141]
 [0.47136644 0.36122063 0.0962846  0.07112823]
 [0.4016079  0.5541043  0.00299673 0.04129104]
 ...
 [0.04700571 0.02121727 0.9142314  0.01754561]
 [0.08600103 0.04419207 0.8370828  0.03272404]
 [0.05421    0.02488171 0.9016793  0.01922905]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 66.26 [%]
Global accuracy score (test) = 58.51 [%]
Global F1 score (train) = 65.57 [%]
Global F1 score (test) = 58.68 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.30      0.33       400
MODERATE-INTENSITY       0.44      0.54      0.48       400
         SEDENTARY       0.72      0.89      0.80       400
VIGOROUS-INTENSITY       0.93      0.61      0.74       345

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

2025-11-04 12:44:01.481969: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:44:01.493305: 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 00:00:1762256641.506581 1422778 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256641.510772 1422778 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256641.520707 1422778 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256641.520727 1422778 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256641.520728 1422778 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256641.520730 1422778 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:44:01.523909: 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 00:00:1762256643.911013 1422778 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256645.589406 1422908 service.cc:152] XLA service 0x78bc40005140 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256645.589432 1422908 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:44:05.628115: 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 00:00:1762256645.799071 1422908 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256648.065800 1422908 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 2/137

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Epoch 3/137

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Epoch 4/137

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Epoch 5/137

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Epoch 6/137

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Epoch 7/137

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Epoch 8/137

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Epoch 9/137

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Epoch 10/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5881 - loss: 0.9163 
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Epoch 11/137

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Epoch 12/137

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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5979 - loss: 0.9304
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Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0778
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5730 - loss: 0.9273 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5838 - loss: 0.9155
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5874 - loss: 0.9126
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5893 - loss: 0.9115
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5902 - loss: 0.9112 - val_accuracy: 0.6127 - val_loss: 0.8319
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.5469 - loss: 0.9199
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5901 - loss: 0.9047 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5924 - loss: 0.9022
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5952 - loss: 0.8999
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5967 - loss: 0.8975
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5975 - loss: 0.8966 - val_accuracy: 0.6189 - val_loss: 0.8122
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7969 - loss: 0.6369
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6283 - loss: 0.8682 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6167 - loss: 0.8731
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6171 - loss: 0.8723
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6169 - loss: 0.8723
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6169 - loss: 0.8722 - val_accuracy: 0.6202 - val_loss: 0.8303
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7905
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6149 - loss: 0.8717 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6106 - loss: 0.8763
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6095 - loss: 0.8751
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6102 - loss: 0.8729
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Epoch 17/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6081 - loss: 0.8533 
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Epoch 18/137

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Epoch 19/137

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 10: 58.51 [%]
F1-score capturado en la ejecución 10: 58.68 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:43[0m 855ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m70/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 731us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 60.09 [%]
Global F1 score (validation) = 59.82 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.46118537 0.38777122 0.0716127  0.07943069]
 [0.46006304 0.38497752 0.10256275 0.05239667]
 [0.43901175 0.5390935  0.00608305 0.01581177]
 ...
 [0.01957805 0.00712397 0.96506596 0.00823204]
 [0.02792252 0.01075998 0.94812244 0.01319506]
 [0.02113395 0.00767357 0.96159506 0.00959746]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 65.87 [%]
Global accuracy score (test) = 58.51 [%]
Global F1 score (train) = 65.0 [%]
Global F1 score (test) = 58.13 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.33      0.35       400
MODERATE-INTENSITY       0.48      0.45      0.46       400
         SEDENTARY       0.66      0.92      0.77       400
VIGOROUS-INTENSITY       0.86      0.67      0.75       345

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

2025-11-04 12:44:29.299425: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:44:29.310864: 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 00:00:1762256669.323837 1425528 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256669.328058 1425528 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256669.338414 1425528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256669.338432 1425528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256669.338433 1425528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256669.338434 1425528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:44:29.341754: 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 00:00:1762256671.700181 1425528 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256673.371009 1425638 service.cc:152] XLA service 0x71111800cd80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256673.371036 1425638 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:44:33.405039: 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 00:00:1762256673.575589 1425638 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256675.786066 1425638 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 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 1.9975
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2805 - loss: 1.9222
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 1.8644
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2939 - loss: 1.8377
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2941 - loss: 1.8364 - val_accuracy: 0.4602 - val_loss: 1.1441
Epoch 2/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2680
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3784 - loss: 1.3359 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3821 - loss: 1.3358
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3841 - loss: 1.3329
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3866 - loss: 1.3265
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3876 - loss: 1.3237 - val_accuracy: 0.5043 - val_loss: 1.0936
Epoch 3/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1722
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4283 - loss: 1.2207 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.2210
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4254 - loss: 1.2177
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4270 - loss: 1.2141
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4281 - loss: 1.2121 - val_accuracy: 0.5125 - val_loss: 1.0473
Epoch 4/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4564 - loss: 1.1655 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1582
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1544
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4615 - loss: 1.1518
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4620 - loss: 1.1508 - val_accuracy: 0.5545 - val_loss: 1.0145
Epoch 5/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0689
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1015 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1063
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1089
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1096
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4936 - loss: 1.1096 - val_accuracy: 0.5726 - val_loss: 0.9819
Epoch 6/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1138 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1054
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.0987
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.0942
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.0924 - val_accuracy: 0.5976 - val_loss: 0.9544
Epoch 7/137

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Epoch 8/137

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[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5176 - loss: 1.0115 
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Epoch 9/137

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[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5648 - loss: 0.9713
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Epoch 10/137

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[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5679 - loss: 0.9667
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[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5732 - loss: 0.9586
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Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9316
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5828 - loss: 0.9553 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5828 - loss: 0.9529
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5844 - loss: 0.9477
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5848 - loss: 0.9434
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5848 - loss: 0.9423 - val_accuracy: 0.6051 - val_loss: 0.8525
Epoch 12/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6307 - loss: 0.8828 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6189 - loss: 0.9010
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6142 - loss: 0.9046
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6118 - loss: 0.9057
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6112 - loss: 0.9062 - val_accuracy: 0.6058 - val_loss: 0.8438
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8362
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6062 - loss: 0.8743 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6085 - loss: 0.8784
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6065 - loss: 0.8822
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6048 - loss: 0.8858
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6045 - loss: 0.8865 - val_accuracy: 0.6219 - val_loss: 0.8221
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.8223
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6104 - loss: 0.8747 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6088 - loss: 0.8768
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6065 - loss: 0.8793
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Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7794
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6155 - loss: 0.8614 
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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6087 - loss: 0.8706
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6086 - loss: 0.8709 - val_accuracy: 0.6068 - val_loss: 0.8234
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8174
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5887 - loss: 0.8915 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5968 - loss: 0.8860
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6001 - loss: 0.8846
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6021 - loss: 0.8832
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6031 - loss: 0.8821 - val_accuracy: 0.6219 - val_loss: 0.8165
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9468
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5943 - loss: 0.8934 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5977 - loss: 0.8856
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6016 - loss: 0.8802
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6048 - loss: 0.8747
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6055 - loss: 0.8736 - val_accuracy: 0.6219 - val_loss: 0.7958
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6562 - loss: 0.8167
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6256 - loss: 0.8558 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6245 - loss: 0.8518
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6246 - loss: 0.8518
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6251 - loss: 0.8514
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6253 - loss: 0.8510 - val_accuracy: 0.6170 - val_loss: 0.8082
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.7829
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6312 - loss: 0.8086 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6248 - loss: 0.8155
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6221 - loss: 0.8201
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6218 - loss: 0.8237
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6218 - loss: 0.8249 - val_accuracy: 0.6002 - val_loss: 0.8171
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.6094 - loss: 0.8486
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6334 - loss: 0.8357 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6319 - loss: 0.8368
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6288 - loss: 0.8375
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6271 - loss: 0.8377
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6268 - loss: 0.8372 - val_accuracy: 0.6209 - val_loss: 0.8012
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0408
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6162 - loss: 0.8658 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6198 - loss: 0.8545
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6220 - loss: 0.8464
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6239 - loss: 0.8412
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6241 - loss: 0.8403 - val_accuracy: 0.6170 - val_loss: 0.8252
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6562 - loss: 0.7393
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6107 - loss: 0.8416 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6171 - loss: 0.8324
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6220 - loss: 0.8253
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6235 - loss: 0.8234
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6237 - loss: 0.8228 - val_accuracy: 0.6147 - val_loss: 0.8226

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 830ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 11: 58.51 [%]
F1-score capturado en la ejecución 11: 58.13 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:50[0m 874ms/step
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[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 723us/step
[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 720us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 761us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 60.55 [%]
Global F1 score (validation) = 61.28 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.49487352 0.43122086 0.03143252 0.04247305]
 [0.5058733  0.4367382  0.0245036  0.03288495]
 [0.41817483 0.5371093  0.00458405 0.04013175]
 ...
 [0.24730162 0.16094291 0.5676485  0.02410699]
 [0.01518703 0.0062054  0.9728827  0.00572491]
 [0.02051464 0.00867963 0.96285474 0.00795103]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 66.8 [%]
Global accuracy score (test) = 58.51 [%]
Global F1 score (train) = 67.17 [%]
Global F1 score (test) = 59.18 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.40      0.39       400
MODERATE-INTENSITY       0.47      0.46      0.46       400
         SEDENTARY       0.68      0.85      0.75       400
VIGOROUS-INTENSITY       0.93      0.64      0.76       345

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

2025-11-04 12:44:58.146734: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:44:58.157828: 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 00:00:1762256698.170794 1428509 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256698.174872 1428509 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256698.184762 1428509 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256698.184777 1428509 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256698.184779 1428509 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256698.184780 1428509 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:44:58.187724: 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 00:00:1762256700.557890 1428509 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256702.214939 1428648 service.cc:152] XLA service 0x7d4d84014360 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256702.214966 1428648 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:45:02.253204: 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 00:00:1762256702.435142 1428648 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256704.651573 1428648 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:29[0m 3s/step - accuracy: 0.2188 - loss: 2.2171
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2593 - loss: 2.0278 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.9416
[1m105/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2849 - loss: 1.8841
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.8317
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2966 - loss: 1.8055
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2967 - loss: 1.8045 - val_accuracy: 0.4619 - val_loss: 1.1462
Epoch 2/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.3543 
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3703 - loss: 1.3397
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3740 - loss: 1.3327
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Epoch 3/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3874 - loss: 1.2509 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3991 - loss: 1.2406
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Epoch 4/137

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[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4703 - loss: 1.1551
[1m104/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1537
[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1519
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Epoch 5/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1120 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1062
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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1074
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Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0015
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.0739 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.0786
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0787
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0782
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5105 - loss: 1.0776 - val_accuracy: 0.5742 - val_loss: 0.9593
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9633
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0508 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0419
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0379
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0355
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5436 - loss: 1.0351 - val_accuracy: 0.5841 - val_loss: 0.9183
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0041
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5546 - loss: 0.9737 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 0.9838
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 0.9887
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5501 - loss: 0.9903
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5504 - loss: 0.9906 - val_accuracy: 0.6074 - val_loss: 0.8844
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6094 - loss: 1.0240
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5621 - loss: 0.9666 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5618 - loss: 0.9628
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5611 - loss: 0.9634
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5612 - loss: 0.9636
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5613 - loss: 0.9635 - val_accuracy: 0.6028 - val_loss: 0.8714
Epoch 10/137

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[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5842 - loss: 0.9293 
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Epoch 11/137

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[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5919 - loss: 0.9139 
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Epoch 12/137

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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6038 - loss: 0.8927
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Epoch 13/137

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[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6022 - loss: 0.8991
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Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9062
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6059 - loss: 0.8722 
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[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6054 - loss: 0.8754
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Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6875 - loss: 0.7257
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6161 - loss: 0.8591 
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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6159 - loss: 0.8670
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6154 - loss: 0.8683
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6155 - loss: 0.8685 - val_accuracy: 0.6304 - val_loss: 0.8143
Epoch 16/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6260 - loss: 0.8355 
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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6219 - loss: 0.8514
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6214 - loss: 0.8507
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6212 - loss: 0.8504 - val_accuracy: 0.6078 - val_loss: 0.8295
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6719 - loss: 0.9395
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6174 - loss: 0.8963 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6196 - loss: 0.8750
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6190 - loss: 0.8634
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Epoch 18/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6123 - loss: 0.8660 
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[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6264 - loss: 0.8460
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Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0318
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6275 - loss: 0.8337 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6272 - loss: 0.8280
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6286 - loss: 0.8250
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6278 - loss: 0.8249
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6273 - loss: 0.8253 - val_accuracy: 0.6199 - val_loss: 0.8009
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9057
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6200 - loss: 0.8353 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6234 - loss: 0.8267
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6257 - loss: 0.8233
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6272 - loss: 0.8230
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6275 - loss: 0.8231 - val_accuracy: 0.6209 - val_loss: 0.8212
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8462
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.8146 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6420 - loss: 0.8071
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6388 - loss: 0.8101
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6374 - loss: 0.8114
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6371 - loss: 0.8120 - val_accuracy: 0.6311 - val_loss: 0.7904
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.5938 - loss: 0.8264
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6316 - loss: 0.8379 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6334 - loss: 0.8280
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6347 - loss: 0.8231
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6352 - loss: 0.8193
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6353 - loss: 0.8187 - val_accuracy: 0.6242 - val_loss: 0.8098
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.7616
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6171 - loss: 0.8327 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6233 - loss: 0.8304
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6257 - loss: 0.8276
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6269 - loss: 0.8252
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6271 - loss: 0.8245 - val_accuracy: 0.6176 - val_loss: 0.8045
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.7983
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6566 - loss: 0.7866 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6505 - loss: 0.7937
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6488 - loss: 0.7942
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6470 - loss: 0.7963
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6464 - loss: 0.7968 - val_accuracy: 0.6252 - val_loss: 0.7999
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6250 - loss: 0.7368
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6554 - loss: 0.7803 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6516 - loss: 0.7890
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6493 - loss: 0.7937
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6469 - loss: 0.7958
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6457 - loss: 0.7966 - val_accuracy: 0.6091 - val_loss: 0.7995
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7918
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6519 - loss: 0.7698 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6478 - loss: 0.7792
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6461 - loss: 0.7851
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6448 - loss: 0.7893
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6444 - loss: 0.7905 - val_accuracy: 0.6235 - val_loss: 0.8119

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 840ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 12: 58.51 [%]
F1-score capturado en la ejecución 12: 59.18 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:42[0m 852ms/step
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 836us/step  
[1m134/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 760us/step
[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 724us/step
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 707us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 793us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.93 [%]
Global F1 score (validation) = 61.87 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.10963368 0.1098198  0.01047742 0.7700691 ]
 [0.1509247  0.17654054 0.00392029 0.6686145 ]
 [0.3816312  0.4732674  0.0058645  0.13923697]
 ...
 [0.10300014 0.05174179 0.80520535 0.04005278]
 [0.01711876 0.00643933 0.9698117  0.00663017]
 [0.02479881 0.00982226 0.9545566  0.0108223 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.41 [%]
Global accuracy score (test) = 57.73 [%]
Global F1 score (train) = 66.88 [%]
Global F1 score (test) = 57.71 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.33      0.34       400
MODERATE-INTENSITY       0.46      0.50      0.48       400
         SEDENTARY       0.69      0.90      0.78       400
VIGOROUS-INTENSITY       0.89      0.58      0.70       345

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

2025-11-04 12:45:28.397958: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:45:28.409337: 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 00:00:1762256728.422987 1431883 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256728.426888 1431883 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256728.436850 1431883 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256728.436869 1431883 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256728.436878 1431883 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256728.436879 1431883 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:45:28.440014: 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 00:00:1762256730.824548 1431883 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256732.530566 1432022 service.cc:152] XLA service 0x7dea9401e5c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256732.530603 1432022 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:45:32.564309: 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 00:00:1762256732.729249 1432022 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256734.937605 1432022 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:29[0m 3s/step - accuracy: 0.2344 - loss: 2.2299
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2535 - loss: 2.0408 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 1.9433
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2782 - loss: 1.8743
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2869 - loss: 1.8203
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2912 - loss: 1.7961 - val_accuracy: 0.4517 - val_loss: 1.1622
Epoch 2/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.3649 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3830 - loss: 1.3568
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3877 - loss: 1.3377
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Epoch 3/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0046
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4529 - loss: 1.1989
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.1961
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Epoch 4/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1693 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1637
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1602
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4644 - loss: 1.1564
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4656 - loss: 1.1541 - val_accuracy: 0.5509 - val_loss: 0.9988
Epoch 5/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1994
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4843 - loss: 1.1193 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1149
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1095
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1049
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1030 - val_accuracy: 0.5542 - val_loss: 0.9737
Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2454
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0713 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0630
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0590
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0574
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5207 - loss: 1.0569 - val_accuracy: 0.5733 - val_loss: 0.9397
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 0.9358
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5653 - loss: 1.0177 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5605 - loss: 1.0224
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 1.0263
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5533 - loss: 1.0274
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5528 - loss: 1.0274 - val_accuracy: 0.5772 - val_loss: 0.9150
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.1606
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5560 - loss: 1.0336 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5571 - loss: 1.0182
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5587 - loss: 1.0112
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5592 - loss: 1.0066
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5596 - loss: 1.0055 - val_accuracy: 0.5959 - val_loss: 0.8787
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.6562 - loss: 0.8600
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5824 - loss: 0.9574 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5749 - loss: 0.9645
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5735 - loss: 0.9676
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5728 - loss: 0.9692
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Epoch 10/137

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Epoch 11/137

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Epoch 12/137

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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6033 - loss: 0.9061
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Epoch 13/137

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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6123 - loss: 0.8936
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Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6562 - loss: 1.0206
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6272 - loss: 0.8809 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6171 - loss: 0.8874
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[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6128 - loss: 0.8897
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Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8754
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6031 - loss: 0.8798 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6018 - loss: 0.8861
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6025 - loss: 0.8844
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6047 - loss: 0.8806
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6053 - loss: 0.8796 - val_accuracy: 0.6163 - val_loss: 0.8255
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.7586
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6247 - loss: 0.8284 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6214 - loss: 0.8357
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6215 - loss: 0.8397
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6216 - loss: 0.8421
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6216 - loss: 0.8430 - val_accuracy: 0.6160 - val_loss: 0.8283
Epoch 17/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6279 - loss: 0.8366 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6201 - loss: 0.8473
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[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6166 - loss: 0.8501
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Epoch 18/137

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[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6405 - loss: 0.8394
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Epoch 19/137

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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6375 - loss: 0.8243
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Epoch 20/137

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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6250 - loss: 0.8395
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6267 - loss: 0.8348
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Epoch 21/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6420 - loss: 0.8147 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6413 - loss: 0.8179
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6401 - loss: 0.8218
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6396 - loss: 0.8223
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Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7159
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6402 - loss: 0.8440 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6374 - loss: 0.8339
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6357 - loss: 0.8302
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6343 - loss: 0.8279
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6341 - loss: 0.8273 - val_accuracy: 0.6255 - val_loss: 0.8000
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.8346
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6509 - loss: 0.7946 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6399 - loss: 0.8034
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6380 - loss: 0.8057
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6381 - loss: 0.8064
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6381 - loss: 0.8064 - val_accuracy: 0.6117 - val_loss: 0.8084
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.7316
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6423 - loss: 0.8036 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6403 - loss: 0.8003
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6400 - loss: 0.7978
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6399 - loss: 0.7976
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6397 - loss: 0.7980 - val_accuracy: 0.6219 - val_loss: 0.8037
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8973
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6536 - loss: 0.8112 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6578 - loss: 0.8000
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6551 - loss: 0.7990
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6526 - loss: 0.7989
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6518 - loss: 0.7990 - val_accuracy: 0.6156 - val_loss: 0.8159
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6562 - loss: 0.8043
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6611 - loss: 0.7930 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6523 - loss: 0.7943
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Epoch 27/137

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 857ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 13: 57.73 [%]
F1-score capturado en la ejecución 13: 57.71 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:39[0m 842ms/step
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 850us/step  
[1m128/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 798us/step
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[1m272/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 746us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m63/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 817us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 62.84 [%]
Global F1 score (validation) = 62.85 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44769573 0.49285564 0.01427245 0.04517615]
 [0.4859227  0.41049787 0.0531849  0.05039447]
 [0.5153541  0.41912994 0.03686418 0.02865183]
 ...
 [0.05011499 0.02078325 0.9065443  0.02255742]
 [0.02343245 0.00831516 0.9578291  0.01042322]
 [0.01926383 0.0065863  0.9656073  0.00854259]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.26 [%]
Global accuracy score (test) = 58.51 [%]
Global F1 score (train) = 67.92 [%]
Global F1 score (test) = 58.84 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.38      0.39       400
MODERATE-INTENSITY       0.46      0.46      0.46       400
         SEDENTARY       0.67      0.86      0.75       400
VIGOROUS-INTENSITY       0.89      0.65      0.75       345

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

2025-11-04 12:45:59.009969: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:45:59.021165: 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 00:00:1762256759.034189 1435348 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256759.038289 1435348 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256759.048149 1435348 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256759.048164 1435348 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256759.048165 1435348 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256759.048166 1435348 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:45:59.051276: 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 00:00:1762256761.417522 1435348 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256763.086156 1435478 service.cc:152] XLA service 0x760ad400c9f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256763.086187 1435478 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:46:03.120956: 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 00:00:1762256763.288995 1435478 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256765.482068 1435478 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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[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2945 - loss: 1.8267
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2990 - loss: 1.7978
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2992 - loss: 1.7968 - val_accuracy: 0.4235 - val_loss: 1.1354
Epoch 2/137

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[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3694 - loss: 1.3552
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Epoch 3/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4219 - loss: 1.0852
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[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4297 - loss: 1.2044
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4304 - loss: 1.2035
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Epoch 4/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4452 - loss: 1.1629
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.1600
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4484 - loss: 1.1590 - val_accuracy: 0.5489 - val_loss: 1.0254
Epoch 5/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1144 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1141
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1149
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1135
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1132 - val_accuracy: 0.5598 - val_loss: 1.0057
Epoch 6/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1032 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5000 - loss: 1.0941
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5036 - loss: 1.0893
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.0868
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5059 - loss: 1.0862 - val_accuracy: 0.5782 - val_loss: 0.9631
Epoch 7/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0591 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0577
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0551
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0531
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Epoch 8/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5592 - loss: 1.0182 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5587 - loss: 1.0130
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 1.0142
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5548 - loss: 1.0137
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Epoch 9/137

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[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5785 - loss: 0.9729 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5755 - loss: 0.9815
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5749 - loss: 0.9832
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5743 - loss: 0.9840
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Epoch 10/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5786 - loss: 0.9460 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5777 - loss: 0.9563
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5774 - loss: 0.9573
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5775 - loss: 0.9580
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5776 - loss: 0.9583 - val_accuracy: 0.6232 - val_loss: 0.8470
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7500 - loss: 0.8559
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5970 - loss: 0.9125 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5912 - loss: 0.9203
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5913 - loss: 0.9219
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5914 - loss: 0.9226
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5913 - loss: 0.9230 - val_accuracy: 0.6245 - val_loss: 0.8352
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9191
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5907 - loss: 0.9266 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5919 - loss: 0.9284
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5924 - loss: 0.9256
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5928 - loss: 0.9243
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5930 - loss: 0.9241 - val_accuracy: 0.6130 - val_loss: 0.8295
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9049
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6073 - loss: 0.9062 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6054 - loss: 0.9063
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6023 - loss: 0.9097
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5996 - loss: 0.9101
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5989 - loss: 0.9099 - val_accuracy: 0.6311 - val_loss: 0.8190
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 0.9298
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6034 - loss: 0.8756 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6084 - loss: 0.8783
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6094 - loss: 0.8826
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6091 - loss: 0.8840
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6089 - loss: 0.8842 - val_accuracy: 0.6206 - val_loss: 0.8191
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.7865
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6163 - loss: 0.8343 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6160 - loss: 0.8370
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6155 - loss: 0.8421
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6146 - loss: 0.8472
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6141 - loss: 0.8494 - val_accuracy: 0.6261 - val_loss: 0.8039
Epoch 16/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6126 - loss: 0.8917 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6122 - loss: 0.8793
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6111 - loss: 0.8757
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6107 - loss: 0.8742
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6108 - loss: 0.8736 - val_accuracy: 0.6209 - val_loss: 0.8044
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7595
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6224 - loss: 0.8146 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6208 - loss: 0.8208
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6201 - loss: 0.8290
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6205 - loss: 0.8344
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6205 - loss: 0.8373 - val_accuracy: 0.6288 - val_loss: 0.7920
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8735
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6305 - loss: 0.8539 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6315 - loss: 0.8499
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6314 - loss: 0.8486
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6306 - loss: 0.8477
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6304 - loss: 0.8475 - val_accuracy: 0.6176 - val_loss: 0.7977
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7789
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6325 - loss: 0.8103 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6290 - loss: 0.8194
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6258 - loss: 0.8261
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6244 - loss: 0.8296
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6238 - loss: 0.8317 - val_accuracy: 0.6314 - val_loss: 0.8029
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7833
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6286 - loss: 0.8336 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6288 - loss: 0.8297
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6275 - loss: 0.8283
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6273 - loss: 0.8279
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6272 - loss: 0.8281 - val_accuracy: 0.6186 - val_loss: 0.8023
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.5766
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6421 - loss: 0.8050 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6362 - loss: 0.8142
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6341 - loss: 0.8194
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6337 - loss: 0.8208
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6333 - loss: 0.8218 - val_accuracy: 0.6245 - val_loss: 0.8121
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.7031 - loss: 0.6124
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6280 - loss: 0.8045 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6307 - loss: 0.8071
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6326 - loss: 0.8074
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6333 - loss: 0.8094
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6334 - loss: 0.8106 - val_accuracy: 0.6281 - val_loss: 0.7928

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 830ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Saved model to disk.

Accuracy capturado en la ejecución 14: 58.51 [%]
F1-score capturado en la ejecución 14: 58.84 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:47[0m 865ms/step
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 779us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.39 [%]
Global F1 score (validation) = 62.5 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.47935566 0.43894085 0.03116476 0.05053873]
 [0.45374894 0.536199   0.00254497 0.00750711]
 [0.45243475 0.31114647 0.11243265 0.1239861 ]
 ...
 [0.02876424 0.0115856  0.9502798  0.00937041]
 [0.01803993 0.00668518 0.96844065 0.00683427]
 [0.01960332 0.00738394 0.9657078  0.007305  ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.1 [%]
Global accuracy score (test) = 58.32 [%]
Global F1 score (train) = 66.76 [%]
Global F1 score (test) = 58.4 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.33      0.34       400
MODERATE-INTENSITY       0.47      0.48      0.48       400
         SEDENTARY       0.68      0.90      0.77       400
VIGOROUS-INTENSITY       0.90      0.64      0.75       345

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

2025-11-04 12:46:27.693066: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:46:27.704421: 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 00:00:1762256787.717650 1438350 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256787.721864 1438350 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256787.731665 1438350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256787.731682 1438350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256787.731683 1438350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256787.731684 1438350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:46:27.734864: 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 00:00:1762256790.145428 1438350 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256791.805877 1438484 service.cc:152] XLA service 0x73b6b40024a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256791.805924 1438484 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:46:31.845428: 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 00:00:1762256792.021006 1438484 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256794.257105 1438484 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:32[0m 3s/step - accuracy: 0.3125 - loss: 2.0740
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2802 - loss: 2.0517 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2902 - loss: 1.9460
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2995 - loss: 1.8738
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3065 - loss: 1.8186
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3089 - loss: 1.7994
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3090 - loss: 1.7982 - val_accuracy: 0.5020 - val_loss: 1.1449
Epoch 2/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4844 - loss: 1.3894
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4029 - loss: 1.3370 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3944 - loss: 1.3361
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3925 - loss: 1.3316
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3930 - loss: 1.3257
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3938 - loss: 1.3226 - val_accuracy: 0.5302 - val_loss: 1.0857
Epoch 3/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4391 - loss: 1.1897 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.1972
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[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4368 - loss: 1.1988
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Epoch 4/137

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[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1539
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4517 - loss: 1.1509
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1468
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Epoch 5/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1001 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.0986
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.0973
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.0961
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4992 - loss: 1.0958 - val_accuracy: 0.5785 - val_loss: 0.9641
Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0486
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0463 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0440
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0429
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0440
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5300 - loss: 1.0443 - val_accuracy: 0.5742 - val_loss: 0.9325
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9342
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 1.0179 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5515 - loss: 1.0194
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5527 - loss: 1.0182
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5531 - loss: 1.0165
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5530 - loss: 1.0159 - val_accuracy: 0.6035 - val_loss: 0.8908
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 1.0416
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5711 - loss: 0.9945 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5687 - loss: 0.9918
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5665 - loss: 0.9913
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5652 - loss: 0.9908
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5650 - loss: 0.9905 - val_accuracy: 0.6048 - val_loss: 0.8579
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7188 - loss: 0.7721
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5982 - loss: 0.9461 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5903 - loss: 0.9505
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5860 - loss: 0.9536
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5841 - loss: 0.9551
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5829 - loss: 0.9561 - val_accuracy: 0.5969 - val_loss: 0.8624
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9457
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5988 - loss: 0.9099 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5974 - loss: 0.9145
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5937 - loss: 0.9198
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5907 - loss: 0.9241
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5901 - loss: 0.9251 - val_accuracy: 0.6055 - val_loss: 0.8432
Epoch 11/137

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Epoch 12/137

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Epoch 13/137

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Epoch 14/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6013 - loss: 0.8838
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6014 - loss: 0.8845
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Epoch 15/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6224 - loss: 0.8725 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6231 - loss: 0.8700
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6217 - loss: 0.8708
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6205 - loss: 0.8710
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6202 - loss: 0.8711 - val_accuracy: 0.6064 - val_loss: 0.8257
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 1.2177
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6182 - loss: 0.9028 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6236 - loss: 0.8806
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6258 - loss: 0.8722
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6259 - loss: 0.8677
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6252 - loss: 0.8668 - val_accuracy: 0.5933 - val_loss: 0.8260
Epoch 17/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6285 - loss: 0.8304 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6261 - loss: 0.8334
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6248 - loss: 0.8365
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6235 - loss: 0.8392
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6230 - loss: 0.8402 - val_accuracy: 0.6104 - val_loss: 0.8157
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.8650
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6143 - loss: 0.8612 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6132 - loss: 0.8592
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6136 - loss: 0.8566
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6143 - loss: 0.8536
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Epoch 19/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6326 - loss: 0.8659 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6325 - loss: 0.8582
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Epoch 20/137

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[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6403 - loss: 0.8154
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Epoch 21/137

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[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6260 - loss: 0.8208
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Epoch 22/137

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[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6408 - loss: 0.7988 
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[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6361 - loss: 0.8083
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6344 - loss: 0.8120
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Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9545
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6257 - loss: 0.8491 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6254 - loss: 0.8401
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6252 - loss: 0.8341
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6262 - loss: 0.8294
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6267 - loss: 0.8283 - val_accuracy: 0.6186 - val_loss: 0.8120
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.6250 - loss: 0.7630
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6449 - loss: 0.7960 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6422 - loss: 0.7988
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6399 - loss: 0.8017
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6392 - loss: 0.8044
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6391 - loss: 0.8048 - val_accuracy: 0.6239 - val_loss: 0.8018
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9585
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6233 - loss: 0.8372 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6275 - loss: 0.8279
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6304 - loss: 0.8225
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6322 - loss: 0.8182
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6326 - loss: 0.8170 - val_accuracy: 0.6045 - val_loss: 0.8199
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.6094 - loss: 0.9009
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6545 - loss: 0.7663 
[1m 67/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6476 - loss: 0.7756
[1m101/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6431 - loss: 0.7837
[1m140/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6417 - loss: 0.7881
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6417 - loss: 0.7899 - val_accuracy: 0.6166 - val_loss: 0.8160
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.7760
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6516 - loss: 0.7822 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6467 - loss: 0.7846
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[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6458 - loss: 0.7848
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Epoch 28/137

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Epoch 29/137

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Saved model to disk.

Accuracy capturado en la ejecución 15: 58.32 [%]
F1-score capturado en la ejecución 15: 58.4 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:39[0m 841ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m70/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 733us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 62.61 [%]
Global F1 score (validation) = 62.29 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.45782977 0.39158872 0.06041455 0.09016699]
 [0.4304471  0.55851394 0.0041894  0.00684956]
 [0.44505203 0.3943969  0.05268647 0.10786458]
 ...
 [0.03819361 0.01808525 0.92973244 0.01398869]
 [0.01882171 0.0079039  0.9666248  0.00664962]
 [0.05480979 0.02709568 0.8914809  0.02661359]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.01 [%]
Global accuracy score (test) = 58.12 [%]
Global F1 score (train) = 66.75 [%]
Global F1 score (test) = 57.96 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.27      0.31       400
MODERATE-INTENSITY       0.47      0.56      0.51       400
         SEDENTARY       0.66      0.86      0.75       400
VIGOROUS-INTENSITY       0.91      0.64      0.75       345

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

2025-11-04 12:46:58.857586: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:46:58.868858: 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 00:00:1762256818.882089 1441991 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256818.886255 1441991 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256818.896223 1441991 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256818.896240 1441991 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256818.896242 1441991 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256818.896243 1441991 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:46:58.899374: 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 00:00:1762256821.273071 1441991 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256822.938657 1442120 service.cc:152] XLA service 0x7d0090004720 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256822.938703 1442120 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:47:02.976878: 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 00:00:1762256823.149149 1442120 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256825.439008 1442120 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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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2986 - loss: 1.8045
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2988 - loss: 1.8034 - val_accuracy: 0.4819 - val_loss: 1.1430
Epoch 2/137

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[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3726 - loss: 1.3523
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3794 - loss: 1.3399
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3820 - loss: 1.3350 - val_accuracy: 0.5168 - val_loss: 1.0886
Epoch 3/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1550
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4386 - loss: 1.2110
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.2072
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4397 - loss: 1.2051 - val_accuracy: 0.5207 - val_loss: 1.0669
Epoch 4/137

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[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1507
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4652 - loss: 1.1475
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4676 - loss: 1.1456
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4686 - loss: 1.1447 - val_accuracy: 0.5601 - val_loss: 1.0152
Epoch 5/137

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[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1034 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4836 - loss: 1.1056
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1049
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.1030
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4910 - loss: 1.1020 - val_accuracy: 0.5545 - val_loss: 0.9990
Epoch 6/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.0966 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0799
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0751
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0717
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5213 - loss: 1.0703 - val_accuracy: 0.5706 - val_loss: 0.9529
Epoch 7/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0372 
[1m 69/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5419 - loss: 1.0317
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0295
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Epoch 8/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5661 - loss: 1.0104 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5610 - loss: 1.0107
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5599 - loss: 1.0093
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5601 - loss: 1.0066
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Epoch 9/137

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[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5621 - loss: 0.9868
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5626 - loss: 0.9872
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5634 - loss: 0.9862
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Epoch 10/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5874 - loss: 0.9302 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5865 - loss: 0.9353
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5877 - loss: 0.9393
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5874 - loss: 0.9428
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5871 - loss: 0.9440 - val_accuracy: 0.6015 - val_loss: 0.8738
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 0.9753
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5887 - loss: 0.9438 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5916 - loss: 0.9407
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5929 - loss: 0.9386
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5931 - loss: 0.9371
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5933 - loss: 0.9361 - val_accuracy: 0.6166 - val_loss: 0.8557
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7659
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5820 - loss: 0.9451 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5844 - loss: 0.9366
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5881 - loss: 0.9274
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5897 - loss: 0.9231
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5900 - loss: 0.9215 - val_accuracy: 0.6009 - val_loss: 0.8573
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9714
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6004 - loss: 0.8859 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6016 - loss: 0.8907
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6008 - loss: 0.8938
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6009 - loss: 0.8966
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6010 - loss: 0.8976 - val_accuracy: 0.6041 - val_loss: 0.8420
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.8942
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6130 - loss: 0.8968 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6177 - loss: 0.8830
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6185 - loss: 0.8789
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6176 - loss: 0.8786
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6170 - loss: 0.8791 - val_accuracy: 0.6176 - val_loss: 0.8480
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8829
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6186 - loss: 0.8790 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6172 - loss: 0.8832
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6161 - loss: 0.8852
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6143 - loss: 0.8864
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6136 - loss: 0.8869 - val_accuracy: 0.6268 - val_loss: 0.8272
Epoch 16/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6154 - loss: 0.8785 
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Epoch 17/137

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Epoch 18/137

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[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6274 - loss: 0.8385
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Epoch 19/137

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[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6199 - loss: 0.8672
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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6202 - loss: 0.8594
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Epoch 20/137

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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6238 - loss: 0.8268
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Epoch 21/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6346 - loss: 0.8188 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6321 - loss: 0.8203
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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6327 - loss: 0.8250
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6327 - loss: 0.8256 - val_accuracy: 0.5953 - val_loss: 0.8349
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7161
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6597 - loss: 0.7942 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6494 - loss: 0.8109
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6439 - loss: 0.8174
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6416 - loss: 0.8191
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6408 - loss: 0.8196 - val_accuracy: 0.5890 - val_loss: 0.8359
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.7081
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6368 - loss: 0.7864 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6351 - loss: 0.8015
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Epoch 24/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6314 - loss: 0.8163 
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[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6312 - loss: 0.8149
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6313 - loss: 0.8146 - val_accuracy: 0.6078 - val_loss: 0.8318
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.7862
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6355 - loss: 0.8111 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.8085
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6428 - loss: 0.8050
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6442 - loss: 0.8031
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6443 - loss: 0.8029 - val_accuracy: 0.6114 - val_loss: 0.8312
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6094 - loss: 0.8287
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6460 - loss: 0.8016 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6463 - loss: 0.7998
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6475 - loss: 0.7989
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6486 - loss: 0.7977
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6490 - loss: 0.7970 - val_accuracy: 0.6255 - val_loss: 0.8113
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.7791
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6327 - loss: 0.8098 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6336 - loss: 0.8093
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6344 - loss: 0.8063
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6353 - loss: 0.8041
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6357 - loss: 0.8032 - val_accuracy: 0.6281 - val_loss: 0.8162
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7579
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6561 - loss: 0.8023 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6569 - loss: 0.7925
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6550 - loss: 0.7927
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6542 - loss: 0.7935
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6536 - loss: 0.7941 - val_accuracy: 0.6048 - val_loss: 0.8239
Epoch 29/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9304
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6279 - loss: 0.7968 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6346 - loss: 0.7936
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6377 - loss: 0.7925
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.7917
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6406 - loss: 0.7915 - val_accuracy: 0.6170 - val_loss: 0.8407
Epoch 30/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0410
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6407 - loss: 0.8082 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6464 - loss: 0.7912
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6480 - loss: 0.7886
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6483 - loss: 0.7877
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6485 - loss: 0.7876 - val_accuracy: 0.6051 - val_loss: 0.8275
Epoch 31/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.9702
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6693 - loss: 0.7647 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6654 - loss: 0.7596
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6612 - loss: 0.7634
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6590 - loss: 0.7660
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6583 - loss: 0.7671 - val_accuracy: 0.6160 - val_loss: 0.8192

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 699ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 58.12 [%]
F1-score capturado en la ejecución 16: 57.96 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:49[0m 872ms/step
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[1m271/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 747us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 739us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.99 [%]
Global F1 score (validation) = 62.19 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.49978986 0.45220318 0.03046081 0.01754615]
 [0.4296635  0.5520794  0.00452841 0.01372865]
 [0.4936981  0.42925265 0.04731632 0.029733  ]
 ...
 [0.02910576 0.01323845 0.94393873 0.01371712]
 [0.02404148 0.0107722  0.9543457  0.01084064]
 [0.02529889 0.01118513 0.9514857  0.01203033]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.9 [%]
Global accuracy score (test) = 59.74 [%]
Global F1 score (train) = 68.66 [%]
Global F1 score (test) = 60.29 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.36      0.38       400
MODERATE-INTENSITY       0.47      0.51      0.48       400
         SEDENTARY       0.70      0.86      0.78       400
VIGOROUS-INTENSITY       0.92      0.67      0.77       345

          accuracy                           0.60      1545
         macro avg       0.62      0.60      0.60      1545
      weighted avg       0.61      0.60      0.60      1545

2025-11-04 12:47:30.967080: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:47:30.978227: 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 00:00:1762256850.991472 1445832 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256850.995837 1445832 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256851.005824 1445832 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256851.005841 1445832 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256851.005843 1445832 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256851.005844 1445832 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:47:31.009039: 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 00:00:1762256853.351388 1445832 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256854.993143 1445942 service.cc:152] XLA service 0x7a997800c760 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256854.993189 1445942 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:47:35.035452: 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 00:00:1762256855.208978 1445942 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256857.483689 1445942 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:37[0m 3s/step - accuracy: 0.1562 - loss: 2.2615
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2737 - loss: 1.9860 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2888 - loss: 1.9052
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2949 - loss: 1.8519
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2997 - loss: 1.8068
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3023 - loss: 1.7836
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3024 - loss: 1.7826 - val_accuracy: 0.4675 - val_loss: 1.1611
Epoch 2/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.3825 
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[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3718 - loss: 1.3546
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Epoch 3/137

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Epoch 4/137

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[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.1690
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Epoch 5/137

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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.0991
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1014
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1017
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Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1544
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0959 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0856
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0793
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0754
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5226 - loss: 1.0735 - val_accuracy: 0.5690 - val_loss: 0.9576
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0153
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0367 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0328
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5322 - loss: 1.0314
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0312
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5356 - loss: 1.0306 - val_accuracy: 0.5792 - val_loss: 0.9160
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.8984
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5603 - loss: 0.9845 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5564 - loss: 0.9919
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5567 - loss: 0.9915
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5575 - loss: 0.9907
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5579 - loss: 0.9901 - val_accuracy: 0.5969 - val_loss: 0.8715
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1426
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5743 - loss: 0.9509 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5750 - loss: 0.9506
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5755 - loss: 0.9516
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5763 - loss: 0.9530
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Epoch 10/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5604 - loss: 0.9813 
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Epoch 11/137

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Epoch 12/137

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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5927 - loss: 0.9082
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Epoch 13/137

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[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5936 - loss: 0.8940
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5946 - loss: 0.8940
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Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9603
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6051 - loss: 0.9194 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6097 - loss: 0.9020
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[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6109 - loss: 0.8910
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Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9554
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6180 - loss: 0.8648 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6189 - loss: 0.8649
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6182 - loss: 0.8641
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6181 - loss: 0.8643
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6179 - loss: 0.8646 - val_accuracy: 0.6199 - val_loss: 0.8025
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9294
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6195 - loss: 0.8591 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6239 - loss: 0.8499
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6235 - loss: 0.8507
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6222 - loss: 0.8518
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6217 - loss: 0.8525 - val_accuracy: 0.6248 - val_loss: 0.8084
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.8173
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6323 - loss: 0.8376 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6291 - loss: 0.8378
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6262 - loss: 0.8413
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6252 - loss: 0.8429
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Epoch 18/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5964 - loss: 0.8146 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6068 - loss: 0.8266
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[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6118 - loss: 0.8327
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6123 - loss: 0.8329 - val_accuracy: 0.6294 - val_loss: 0.7857
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7819
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6184 - loss: 0.8333 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6227 - loss: 0.8322
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6250 - loss: 0.8300
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6257 - loss: 0.8292
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6259 - loss: 0.8293 - val_accuracy: 0.6245 - val_loss: 0.7938
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.6977
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6527 - loss: 0.7775 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6438 - loss: 0.7973
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6392 - loss: 0.8076
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6371 - loss: 0.8132
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6363 - loss: 0.8153 - val_accuracy: 0.6117 - val_loss: 0.8096
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.5312 - loss: 0.9239
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6098 - loss: 0.8614 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6152 - loss: 0.8514
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6176 - loss: 0.8444
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6196 - loss: 0.8405
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6203 - loss: 0.8390 - val_accuracy: 0.6248 - val_loss: 0.8115
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.7666
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6312 - loss: 0.8092 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6304 - loss: 0.8168
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6290 - loss: 0.8185
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6289 - loss: 0.8190
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6291 - loss: 0.8190 - val_accuracy: 0.6130 - val_loss: 0.8040

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 797ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 17: 59.74 [%]
F1-score capturado en la ejecución 17: 60.29 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:39[0m 842ms/step
[1m 60/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 851us/step  
[1m124/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 816us/step
[1m195/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 777us/step
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 783us/step
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 790us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 765us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.5 [%]
Global F1 score (validation) = 61.71 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44247377 0.43592775 0.02578563 0.09581286]
 [0.44578972 0.34456736 0.10874416 0.10089878]
 [0.44928858 0.51626164 0.00740873 0.02704107]
 ...
 [0.07367589 0.03336225 0.8715854  0.02137646]
 [0.02658275 0.00984703 0.9567715  0.00679879]
 [0.02930932 0.01092743 0.9515959  0.00816733]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 66.43 [%]
Global accuracy score (test) = 58.51 [%]
Global F1 score (train) = 66.03 [%]
Global F1 score (test) = 58.68 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.36      0.38       400
MODERATE-INTENSITY       0.46      0.48      0.47       400
         SEDENTARY       0.68      0.89      0.77       400
VIGOROUS-INTENSITY       0.90      0.61      0.73       345

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

2025-11-04 12:47:59.700344: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:47:59.712196: 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 00:00:1762256879.725636 1448819 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256879.729670 1448819 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256879.739324 1448819 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256879.739341 1448819 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256879.739343 1448819 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256879.739344 1448819 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:47:59.742533: 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 00:00:1762256882.092567 1448819 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256883.765258 1448950 service.cc:152] XLA service 0x79dbb8004fb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256883.765304 1448950 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:48:03.800030: 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 00:00:1762256883.963867 1448950 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256886.198684 1448950 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:28[0m 3s/step - accuracy: 0.3281 - loss: 1.8947
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 2.0283 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.9423
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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3046 - loss: 1.8353
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.3072 - loss: 1.8137
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3074 - loss: 1.8127 - val_accuracy: 0.4438 - val_loss: 1.1872
Epoch 2/137

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[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.3935 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.3813
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3697 - loss: 1.3707
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3745 - loss: 1.3598
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3751 - loss: 1.3585 - val_accuracy: 0.4977 - val_loss: 1.1101
Epoch 3/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4114 - loss: 1.2355 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4115 - loss: 1.2396
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4135 - loss: 1.2375
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4165 - loss: 1.2329
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4177 - loss: 1.2311 - val_accuracy: 0.5214 - val_loss: 1.0650
Epoch 4/137

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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.1613
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1596
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4601 - loss: 1.1588 - val_accuracy: 0.5555 - val_loss: 1.0185
Epoch 5/137

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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4972 - loss: 1.1116 - val_accuracy: 0.5742 - val_loss: 0.9868
Epoch 6/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0851 
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[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0727
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0704
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Epoch 7/137

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[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0428
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5393 - loss: 1.0416
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0390
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Epoch 8/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5476 - loss: 1.0062 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5497 - loss: 1.0139
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[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 1.0096
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5549 - loss: 1.0087 - val_accuracy: 0.6018 - val_loss: 0.8791
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5781 - loss: 0.8763
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5942 - loss: 0.9689 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5949 - loss: 0.9734
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5930 - loss: 0.9744
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5906 - loss: 0.9738
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5899 - loss: 0.9733 - val_accuracy: 0.5992 - val_loss: 0.8698
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6250 - loss: 0.9451
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5774 - loss: 0.9586 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5794 - loss: 0.9604
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5806 - loss: 0.9591
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5812 - loss: 0.9571
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5815 - loss: 0.9564 - val_accuracy: 0.6147 - val_loss: 0.8473
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6875 - loss: 0.8299
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6302 - loss: 0.9095 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6207 - loss: 0.9105
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6157 - loss: 0.9124
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6121 - loss: 0.9155
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6109 - loss: 0.9163 - val_accuracy: 0.6124 - val_loss: 0.8558
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.6250 - loss: 0.9958
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6355 - loss: 0.8755 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6239 - loss: 0.8890
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6188 - loss: 0.8936
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6152 - loss: 0.8969
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6136 - loss: 0.8990 - val_accuracy: 0.6032 - val_loss: 0.8469
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5156 - loss: 1.0534
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5883 - loss: 0.9112 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5951 - loss: 0.9035
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5971 - loss: 0.9018
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5989 - loss: 0.9002
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Epoch 14/137

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Epoch 15/137

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[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6095 - loss: 0.8890
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[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6099 - loss: 0.8824
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Epoch 16/137

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[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6281 - loss: 0.8554
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Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7498
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6297 - loss: 0.8467 
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6292 - loss: 0.8418
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6288 - loss: 0.8427
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Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8104
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6342 - loss: 0.8228 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6325 - loss: 0.8273
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6319 - loss: 0.8312
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6312 - loss: 0.8340
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6310 - loss: 0.8350 - val_accuracy: 0.6255 - val_loss: 0.8037
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 0.7091
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6196 - loss: 0.8404 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6184 - loss: 0.8447
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6187 - loss: 0.8466
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6182 - loss: 0.8478
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6183 - loss: 0.8475 - val_accuracy: 0.6209 - val_loss: 0.8125
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7344 - loss: 0.7525
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6489 - loss: 0.8223 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6441 - loss: 0.8251
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6414 - loss: 0.8265
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6389 - loss: 0.8274
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6384 - loss: 0.8277 - val_accuracy: 0.6101 - val_loss: 0.8221
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7948
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6369 - loss: 0.7951 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6357 - loss: 0.8030
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6343 - loss: 0.8077
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6327 - loss: 0.8119
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Epoch 22/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6195 - loss: 0.8252 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6230 - loss: 0.8303
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6258 - loss: 0.8286
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6272 - loss: 0.8273
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6276 - loss: 0.8266 - val_accuracy: 0.6298 - val_loss: 0.7987
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6562 - loss: 0.9649
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6500 - loss: 0.7989 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6429 - loss: 0.8046
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6412 - loss: 0.8066
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6401 - loss: 0.8082
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6396 - loss: 0.8088 - val_accuracy: 0.6133 - val_loss: 0.8092
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7493
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6315 - loss: 0.8044 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6314 - loss: 0.8109
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6317 - loss: 0.8117
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6315 - loss: 0.8120
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6314 - loss: 0.8123 - val_accuracy: 0.6219 - val_loss: 0.8157
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8651
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6359 - loss: 0.8209 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6363 - loss: 0.8173
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6372 - loss: 0.8137
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6378 - loss: 0.8120
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6381 - loss: 0.8114 - val_accuracy: 0.6156 - val_loss: 0.8207
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.6406 - loss: 0.9411
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6582 - loss: 0.8186 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6511 - loss: 0.8135
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6482 - loss: 0.8121
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6462 - loss: 0.8118
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6456 - loss: 0.8121 - val_accuracy: 0.6206 - val_loss: 0.8016
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7500 - loss: 0.6429
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6743 - loss: 0.7769 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6654 - loss: 0.7814
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6569 - loss: 0.7854 - val_accuracy: 0.6107 - val_loss: 0.8009

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 818ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 58.51 [%]
F1-score capturado en la ejecución 18: 58.68 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 759us/step  
[1m139/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 729us/step
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 713us/step
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 706us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 741us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 60.97 [%]
Global F1 score (validation) = 61.19 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.39217854 0.4030292  0.02219541 0.18259692]
 [0.48305136 0.39306715 0.04780184 0.0760796 ]
 [0.47708455 0.480381   0.00970239 0.03283209]
 ...
 [0.0306185  0.01290494 0.94104224 0.01543438]
 [0.02646897 0.01099556 0.95031196 0.01222353]
 [0.03239974 0.01377685 0.9377682  0.0160552 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.53 [%]
Global accuracy score (test) = 58.25 [%]
Global F1 score (train) = 68.82 [%]
Global F1 score (test) = 58.47 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.47      0.43       400
MODERATE-INTENSITY       0.46      0.34      0.39       400
         SEDENTARY       0.67      0.86      0.75       400
VIGOROUS-INTENSITY       0.90      0.66      0.76       345

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

2025-11-04 12:48:30.092186: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:48:30.104011: 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 00:00:1762256910.117713 1452278 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256910.121993 1452278 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256910.132142 1452278 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256910.132166 1452278 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256910.132167 1452278 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256910.132168 1452278 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:48:30.135532: 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 00:00:1762256912.489479 1452278 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256914.130040 1452408 service.cc:152] XLA service 0x70fa0801d0d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256914.130067 1452408 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:48:34.164206: 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 00:00:1762256914.327747 1452408 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256916.554735 1452408 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:24[0m 3s/step - accuracy: 0.1719 - loss: 2.0060
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2711 - loss: 2.0055 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2857 - loss: 1.9209
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2934 - loss: 1.8659
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3006 - loss: 1.8131
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3033 - loss: 1.7935
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3035 - loss: 1.7924 - val_accuracy: 0.5023 - val_loss: 1.1143
Epoch 2/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3754 - loss: 1.3540 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3799 - loss: 1.3440
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3838 - loss: 1.3352
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3866 - loss: 1.3257
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3875 - loss: 1.3225 - val_accuracy: 0.5013 - val_loss: 1.0821
Epoch 3/137

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[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4475 - loss: 1.1933 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4412 - loss: 1.2022
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4416 - loss: 1.1998
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.1970
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4431 - loss: 1.1957 - val_accuracy: 0.5394 - val_loss: 1.0334
Epoch 4/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.1796 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.1693
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Epoch 5/137

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[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5023 - loss: 1.0848
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.0863
[1m139/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.0874
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Epoch 6/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0725 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.0677
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0645
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0618
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5270 - loss: 1.0608 - val_accuracy: 0.5907 - val_loss: 0.9193
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0538
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5443 - loss: 1.0505 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5487 - loss: 1.0379
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0335
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5495 - loss: 1.0289
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5500 - loss: 1.0273 - val_accuracy: 0.6005 - val_loss: 0.8981
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 0.9714
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5598 - loss: 0.9829 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 0.9828
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 0.9840
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5586 - loss: 0.9859
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5585 - loss: 0.9865 - val_accuracy: 0.5972 - val_loss: 0.8790
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 1.0256
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5778 - loss: 0.9711 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5764 - loss: 0.9675
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5777 - loss: 0.9648
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5783 - loss: 0.9634
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5783 - loss: 0.9631 - val_accuracy: 0.5762 - val_loss: 0.8801
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9417
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6023 - loss: 0.9447 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6007 - loss: 0.9379
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5964 - loss: 0.9374
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5938 - loss: 0.9368
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5929 - loss: 0.9369 - val_accuracy: 0.6048 - val_loss: 0.8565
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0079
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5774 - loss: 0.9510 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5857 - loss: 0.9296
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5901 - loss: 0.9223
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5908 - loss: 0.9212
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5909 - loss: 0.9207 - val_accuracy: 0.6199 - val_loss: 0.8449
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8510
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Epoch 13/137

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Epoch 14/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6116 - loss: 0.8733
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Epoch 15/137

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[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5965 - loss: 0.8805
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5996 - loss: 0.8770
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Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7031 - loss: 0.7521
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6263 - loss: 0.8517 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6231 - loss: 0.8568
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6207 - loss: 0.8584
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6201 - loss: 0.8581
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6200 - loss: 0.8581 - val_accuracy: 0.6170 - val_loss: 0.8146
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8133
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6119 - loss: 0.8517 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6211 - loss: 0.8431
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6230 - loss: 0.8420
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6225 - loss: 0.8429
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6226 - loss: 0.8431 - val_accuracy: 0.6064 - val_loss: 0.8293
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7904
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6349 - loss: 0.8032 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6312 - loss: 0.8154
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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6256 - loss: 0.8270
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6248 - loss: 0.8286 - val_accuracy: 0.6242 - val_loss: 0.8177
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7232
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6500 - loss: 0.7923 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6419 - loss: 0.8085
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6382 - loss: 0.8151
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Epoch 20/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6259 - loss: 0.8193 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6284 - loss: 0.8214
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 824ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 19: 58.25 [%]
F1-score capturado en la ejecución 19: 58.47 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:52[0m 880ms/step
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 747us/step  
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 718us/step
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 711us/step
[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 717us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 773us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 61.96 [%]
Global F1 score (validation) = 62.28 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4517314  0.5358623  0.00264795 0.00975841]
 [0.42286763 0.38448754 0.05712117 0.13552372]
 [0.48876464 0.4623412  0.02558041 0.02331376]
 ...
 [0.03157847 0.01258289 0.94268805 0.01315061]
 [0.02749923 0.0106864  0.95097095 0.01084348]
 [0.02704413 0.01046087 0.95226836 0.01022665]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 66.34 [%]
Global accuracy score (test) = 57.28 [%]
Global F1 score (train) = 66.1 [%]
Global F1 score (test) = 57.92 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.36      0.36       400
MODERATE-INTENSITY       0.45      0.47      0.46       400
         SEDENTARY       0.69      0.84      0.76       400
VIGOROUS-INTENSITY       0.89      0.63      0.74       345

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

2025-11-04 12:48:58.094752: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:48:58.106088: 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 00:00:1762256938.119155 1455111 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256938.123191 1455111 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256938.133108 1455111 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256938.133124 1455111 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256938.133125 1455111 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256938.133126 1455111 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:48:58.136269: 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 00:00:1762256940.492312 1455111 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256942.139338 1455222 service.cc:152] XLA service 0x7b045400dda0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256942.139363 1455222 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:49:02.172148: 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 00:00:1762256942.336296 1455222 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256944.565281 1455222 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 2/137

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Epoch 3/137

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Epoch 4/137

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[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1631
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1596
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Epoch 5/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1195 
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1163
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1135
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1128 - val_accuracy: 0.5664 - val_loss: 0.9639
Epoch 6/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.0932 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0908
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0866
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0830
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5154 - loss: 1.0808 - val_accuracy: 0.5877 - val_loss: 0.9288
Epoch 7/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0435 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0334
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0301
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0291
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Epoch 8/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5679 - loss: 0.9890 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5663 - loss: 0.9919
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Epoch 9/137

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Epoch 10/137

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Epoch 11/137

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Epoch 12/137

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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5943 - loss: 0.9120
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Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8019
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6012 - loss: 0.9172 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6018 - loss: 0.9141
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6039 - loss: 0.9073
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6039 - loss: 0.9050
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6041 - loss: 0.9041 - val_accuracy: 0.6239 - val_loss: 0.8169
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1319
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5866 - loss: 0.9054 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5930 - loss: 0.8967
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5976 - loss: 0.8932
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6005 - loss: 0.8907
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6012 - loss: 0.8901 - val_accuracy: 0.6294 - val_loss: 0.8083
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6719 - loss: 0.8218
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6034 - loss: 0.8903 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6089 - loss: 0.8833
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6110 - loss: 0.8782
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6119 - loss: 0.8758
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6120 - loss: 0.8752 - val_accuracy: 0.6307 - val_loss: 0.7984
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5000 - loss: 0.9014
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5941 - loss: 0.8897 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6054 - loss: 0.8745
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[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6134 - loss: 0.8620
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Epoch 17/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6158 - loss: 0.8633 
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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6193 - loss: 0.8615
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6194 - loss: 0.8611 - val_accuracy: 0.6130 - val_loss: 0.7891
Epoch 18/137

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[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6104 - loss: 0.8633 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6149 - loss: 0.8512
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6165 - loss: 0.8474
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6173 - loss: 0.8459
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Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9339
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6189 - loss: 0.8926 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6219 - loss: 0.8800
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6224 - loss: 0.8738
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6233 - loss: 0.8684
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Epoch 20/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6132 - loss: 0.8508 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6199 - loss: 0.8431
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6214 - loss: 0.8417
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6222 - loss: 0.8410
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6224 - loss: 0.8412 - val_accuracy: 0.6202 - val_loss: 0.7903
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.8152
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6506 - loss: 0.8072 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6437 - loss: 0.8129
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.8151
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6371 - loss: 0.8165
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6359 - loss: 0.8177 - val_accuracy: 0.6445 - val_loss: 0.7822
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9837
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6172 - loss: 0.8519 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6215 - loss: 0.8394
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6242 - loss: 0.8339
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6256 - loss: 0.8309
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6260 - loss: 0.8301 - val_accuracy: 0.6321 - val_loss: 0.7803
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.7031 - loss: 0.7093
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6296 - loss: 0.8241 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6301 - loss: 0.8244
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6321 - loss: 0.8198
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6329 - loss: 0.8185
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6330 - loss: 0.8182 - val_accuracy: 0.6160 - val_loss: 0.7869
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.6094 - loss: 0.9961
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6201 - loss: 0.8418 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6260 - loss: 0.8299
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6301 - loss: 0.8234
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6319 - loss: 0.8211
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6325 - loss: 0.8198 - val_accuracy: 0.6370 - val_loss: 0.7761
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5625 - loss: 0.8841
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6288 - loss: 0.7936 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6334 - loss: 0.7920
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6345 - loss: 0.7944
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6346 - loss: 0.7981
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6345 - loss: 0.7993 - val_accuracy: 0.6390 - val_loss: 0.7749
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6719 - loss: 0.6347
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6418 - loss: 0.7760 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.7894
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6395 - loss: 0.7946
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6395 - loss: 0.7966
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6394 - loss: 0.7975 - val_accuracy: 0.6337 - val_loss: 0.7688
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.6799
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6329 - loss: 0.7761 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6381 - loss: 0.7806
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6399 - loss: 0.7837
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6403 - loss: 0.7869
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6404 - loss: 0.7876 - val_accuracy: 0.6284 - val_loss: 0.7676
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.7609
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[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6444 - loss: 0.7717
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6439 - loss: 0.7756
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6437 - loss: 0.7783
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6436 - loss: 0.7791 - val_accuracy: 0.6367 - val_loss: 0.7758
Epoch 29/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.7454
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6404 - loss: 0.7899 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6410 - loss: 0.7901
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6408 - loss: 0.7904
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6407 - loss: 0.7906
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6407 - loss: 0.7905 - val_accuracy: 0.6321 - val_loss: 0.7863
Epoch 30/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7939
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6645 - loss: 0.7576 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6620 - loss: 0.7642
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6595 - loss: 0.7690
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6573 - loss: 0.7734
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6560 - loss: 0.7755 - val_accuracy: 0.6291 - val_loss: 0.7782
Epoch 31/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7031 - loss: 0.6444
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6545 - loss: 0.7493 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6520 - loss: 0.7603
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6500 - loss: 0.7665
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6488 - loss: 0.7707
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6484 - loss: 0.7721 - val_accuracy: 0.6242 - val_loss: 0.7701
Epoch 32/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.8542
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6388 - loss: 0.7945 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6429 - loss: 0.7876
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6432 - loss: 0.7859
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6430 - loss: 0.7861
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6433 - loss: 0.7862 - val_accuracy: 0.6386 - val_loss: 0.7802

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 676ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 57.28 [%]
F1-score capturado en la ejecución 20: 57.92 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:46[0m 863ms/step
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m70/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 732us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 63.76 [%]
Global F1 score (validation) = 62.92 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4323994  0.516666   0.01518926 0.03574528]
 [0.4594231  0.417064   0.06425666 0.05925627]
 [0.12471551 0.08042698 0.20225406 0.5926034 ]
 ...
 [0.08541103 0.04090981 0.83350015 0.04017894]
 [0.04887227 0.02103754 0.910923   0.01916724]
 [0.04664343 0.01990718 0.91609275 0.01735663]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.71 [%]
Global accuracy score (test) = 59.81 [%]
Global F1 score (train) = 65.53 [%]
Global F1 score (test) = 59.44 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.26      0.30       400
MODERATE-INTENSITY       0.50      0.65      0.56       400
         SEDENTARY       0.69      0.86      0.77       400
VIGOROUS-INTENSITY       0.92      0.63      0.75       345

          accuracy                           0.60      1545
         macro avg       0.62      0.60      0.59      1545
      weighted avg       0.61      0.60      0.59      1545

2025-11-04 12:49:30.466842: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:49:30.478073: 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 00:00:1762256970.491481 1459035 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256970.495595 1459035 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256970.505372 1459035 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256970.505391 1459035 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256970.505393 1459035 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256970.505395 1459035 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:49:30.508507: 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 00:00:1762256972.885933 1459035 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762256974.576345 1459164 service.cc:152] XLA service 0x706f98004ca0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762256974.576377 1459164 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:49:34.610684: 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 00:00:1762256974.783717 1459164 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762256976.995705 1459164 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:28[0m 3s/step - accuracy: 0.3750 - loss: 1.7006
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2817 - loss: 1.9378 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2806 - loss: 1.9012
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 1.8556
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 1.8159
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2932 - loss: 1.7927
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2933 - loss: 1.7917 - val_accuracy: 0.4501 - val_loss: 1.1698
Epoch 2/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.4068
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.3938 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.3844
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[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3627 - loss: 1.3615
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3653 - loss: 1.3561 - val_accuracy: 0.5049 - val_loss: 1.0927
Epoch 3/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4086 - loss: 1.2657 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4182 - loss: 1.2509
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2431
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2367
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Epoch 4/137

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[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4453 - loss: 1.1802
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.1746
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Epoch 5/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1171 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1191
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1192
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1163
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Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 1.1339
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0843 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0865
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0804
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0746
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5218 - loss: 1.0735 - val_accuracy: 0.6012 - val_loss: 0.9153
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9496
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0318 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0283
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0280
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0274
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5440 - loss: 1.0271 - val_accuracy: 0.5926 - val_loss: 0.9016
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9784
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5628 - loss: 0.9919 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5614 - loss: 0.9992
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5615 - loss: 1.0008
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5620 - loss: 1.0001
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5624 - loss: 0.9993 - val_accuracy: 0.6124 - val_loss: 0.8776
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9832
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5779 - loss: 0.9491 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5811 - loss: 0.9434
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5817 - loss: 0.9432
[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5812 - loss: 0.9458
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5810 - loss: 0.9472 - val_accuracy: 0.5884 - val_loss: 0.8572
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0591
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5662 - loss: 0.9819 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5746 - loss: 0.9603
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5779 - loss: 0.9532
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5805 - loss: 0.9489
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Epoch 11/137

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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5922 - loss: 0.9104
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5926 - loss: 0.9112
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Epoch 12/137

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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5846 - loss: 0.9375
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[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5888 - loss: 0.9310
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Epoch 13/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6140 - loss: 0.9015 
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6044 - loss: 0.9023 - val_accuracy: 0.6288 - val_loss: 0.8296
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 0.8650
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6314 - loss: 0.8566 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6198 - loss: 0.8668
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6153 - loss: 0.8696
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6132 - loss: 0.8716
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6127 - loss: 0.8720 - val_accuracy: 0.6222 - val_loss: 0.8256
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.6562 - loss: 0.8514
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6134 - loss: 0.9076 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6119 - loss: 0.8987
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6127 - loss: 0.8924
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6132 - loss: 0.8881
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6129 - loss: 0.8873 - val_accuracy: 0.6114 - val_loss: 0.8210
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8425
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6225 - loss: 0.8557 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6193 - loss: 0.8586
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6195 - loss: 0.8597
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6194 - loss: 0.8607
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6194 - loss: 0.8610 - val_accuracy: 0.6291 - val_loss: 0.8122
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9539
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6020 - loss: 0.8713 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6091 - loss: 0.8620
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6134 - loss: 0.8577
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6159 - loss: 0.8551
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6162 - loss: 0.8547 - val_accuracy: 0.6468 - val_loss: 0.8060
Epoch 18/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6384 - loss: 0.8151 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6373 - loss: 0.8250
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6358 - loss: 0.8286
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6340 - loss: 0.8315
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Epoch 19/137

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[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6123 - loss: 0.8308 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6146 - loss: 0.8369
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6152 - loss: 0.8412
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6163 - loss: 0.8423
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6167 - loss: 0.8425 - val_accuracy: 0.6055 - val_loss: 0.8205
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8912
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6350 - loss: 0.8297 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6288 - loss: 0.8381
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6255 - loss: 0.8415
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6244 - loss: 0.8418
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6244 - loss: 0.8417 - val_accuracy: 0.6327 - val_loss: 0.8097
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6094 - loss: 0.9169
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6280 - loss: 0.8470 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6298 - loss: 0.8383
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6288 - loss: 0.8375
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6280 - loss: 0.8377
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6279 - loss: 0.8372 - val_accuracy: 0.6390 - val_loss: 0.8071
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 0.9404
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6383 - loss: 0.7969 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6355 - loss: 0.7973
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6341 - loss: 0.8008
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6338 - loss: 0.8032
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6336 - loss: 0.8044 - val_accuracy: 0.6140 - val_loss: 0.8148

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 811ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Saved model to disk.

Accuracy capturado en la ejecución 21: 59.81 [%]
F1-score capturado en la ejecución 21: 59.44 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:44[0m 857ms/step
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 845us/step  
[1m125/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 814us/step
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 795us/step
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 778us/step
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 761us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m72/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 713us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.3 [%]
Global F1 score (validation) = 61.52 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4490605  0.407182   0.03097699 0.11278055]
 [0.47358444 0.46644065 0.02596118 0.0340137 ]
 [0.4585455  0.52029216 0.00996405 0.0111983 ]
 ...
 [0.04161661 0.01845911 0.9185897  0.02133461]
 [0.02335973 0.00971695 0.95604515 0.01087816]
 [0.01831736 0.0074035  0.96673757 0.00754162]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 66.74 [%]
Global accuracy score (test) = 56.96 [%]
Global F1 score (train) = 66.18 [%]
Global F1 score (test) = 56.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.33      0.29      0.31       400
MODERATE-INTENSITY       0.44      0.45      0.44       400
         SEDENTARY       0.68      0.89      0.77       400
VIGOROUS-INTENSITY       0.87      0.66      0.75       345

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

2025-11-04 12:49:59.202217: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:49:59.213407: 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 00:00:1762256999.226640 1462035 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762256999.230812 1462035 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762256999.240844 1462035 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256999.240861 1462035 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256999.240863 1462035 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762256999.240864 1462035 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:49:59.243968: 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 00:00:1762257001.642892 1462035 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762257003.296345 1462174 service.cc:152] XLA service 0x7aac2c004de0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762257003.296392 1462174 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:50:03.332335: 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 00:00:1762257003.505879 1462174 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762257005.701840 1462174 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:23[0m 3s/step - accuracy: 0.2031 - loss: 2.0508
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2892 - loss: 1.9580 
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[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3030 - loss: 1.8519
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3068 - loss: 1.8118
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3085 - loss: 1.7950
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3086 - loss: 1.7940 - val_accuracy: 0.4382 - val_loss: 1.1453
Epoch 2/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3729 - loss: 1.4025 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3697 - loss: 1.3946
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3712 - loss: 1.3857
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3731 - loss: 1.3776
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3749 - loss: 1.3713 - val_accuracy: 0.4793 - val_loss: 1.0967
Epoch 3/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4064 - loss: 1.2415 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4117 - loss: 1.2405
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4182 - loss: 1.2353
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4236 - loss: 1.2298
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4251 - loss: 1.2280 - val_accuracy: 0.5335 - val_loss: 1.0338
Epoch 4/137

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[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4462 - loss: 1.1672
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4497 - loss: 1.1662
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4535 - loss: 1.1638
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4546 - loss: 1.1628 - val_accuracy: 0.5437 - val_loss: 1.0198
Epoch 5/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1255 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1127
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1088
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1049
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Epoch 6/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0700 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0614
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0578
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0558
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Epoch 7/137

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[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0464
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0381
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0312
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Epoch 8/137

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[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5711 - loss: 0.9792
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5700 - loss: 0.9808
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5704 - loss: 0.9801
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5705 - loss: 0.9796 - val_accuracy: 0.6153 - val_loss: 0.8520
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 0.9528
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5706 - loss: 0.9817 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5730 - loss: 0.9801
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5727 - loss: 0.9784
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5736 - loss: 0.9752
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5736 - loss: 0.9741 - val_accuracy: 0.6133 - val_loss: 0.8481
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9189
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5860 - loss: 0.9521 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5829 - loss: 0.9533
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5830 - loss: 0.9531
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5835 - loss: 0.9517
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5836 - loss: 0.9512 - val_accuracy: 0.6064 - val_loss: 0.8408
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9746
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5825 - loss: 0.9222 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5853 - loss: 0.9219
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5862 - loss: 0.9233
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5865 - loss: 0.9243
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5867 - loss: 0.9243 - val_accuracy: 0.6199 - val_loss: 0.8362
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.9008
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5972 - loss: 0.9245 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6012 - loss: 0.9152
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6033 - loss: 0.9119
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6040 - loss: 0.9115
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6040 - loss: 0.9116 - val_accuracy: 0.6120 - val_loss: 0.8282
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 0.9831
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5947 - loss: 0.8922 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5999 - loss: 0.8851
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6024 - loss: 0.8822
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6026 - loss: 0.8835
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6026 - loss: 0.8841 - val_accuracy: 0.6189 - val_loss: 0.8329
Epoch 14/137

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Epoch 15/137

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Epoch 16/137

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Epoch 17/137

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Epoch 18/137

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Epoch 19/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6365 - loss: 0.8111 
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6325 - loss: 0.8227
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Epoch 20/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6241 - loss: 0.8410 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6273 - loss: 0.8366
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6266 - loss: 0.8365
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6256 - loss: 0.8369
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Epoch 21/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6356 - loss: 0.8103 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6306 - loss: 0.8206
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Epoch 22/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6522 - loss: 0.8130 
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Epoch 23/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6379 - loss: 0.7999 
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6362 - loss: 0.8060
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Epoch 24/137

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[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6421 - loss: 0.8033
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Epoch 25/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6152 - loss: 0.8859 
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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6267 - loss: 0.8366
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Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7724
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6646 - loss: 0.7736 
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[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6519 - loss: 0.7877
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6490 - loss: 0.7918
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Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6875 - loss: 0.6679
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6488 - loss: 0.7463 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6415 - loss: 0.7670
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6391 - loss: 0.7795
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6390 - loss: 0.7809 - val_accuracy: 0.6117 - val_loss: 0.8109
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8423
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6262 - loss: 0.8118 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6343 - loss: 0.8021
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6395 - loss: 0.7973
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6421 - loss: 0.7950
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6430 - loss: 0.7942 - val_accuracy: 0.6291 - val_loss: 0.7969
Epoch 29/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8002
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6269 - loss: 0.8196 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6307 - loss: 0.8099
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6343 - loss: 0.8035
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6367 - loss: 0.7993
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6372 - loss: 0.7986 - val_accuracy: 0.6199 - val_loss: 0.8288
Epoch 30/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.9912
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6576 - loss: 0.8368 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6508 - loss: 0.8206
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6483 - loss: 0.8055
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Epoch 31/137

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Epoch 32/137

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Epoch 33/137

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[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6593 - loss: 0.7648
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 678ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 22: 56.96 [%]
F1-score capturado en la ejecución 22: 56.95 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:38[0m 840ms/step
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[1m128/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 794us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 758us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.29 [%]
Global F1 score (validation) = 62.24 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.49234438 0.33344403 0.13759005 0.03662153]
 [0.44802025 0.53124785 0.00777317 0.01295865]
 [0.4930767  0.45408046 0.02976013 0.02308266]
 ...
 [0.05016735 0.02103795 0.90506965 0.02372511]
 [0.08543909 0.03912224 0.8402051  0.03523359]
 [0.16418046 0.08778694 0.6904133  0.05761929]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.78 [%]
Global accuracy score (test) = 60.06 [%]
Global F1 score (train) = 66.73 [%]
Global F1 score (test) = 60.58 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.32      0.34       400
MODERATE-INTENSITY       0.48      0.61      0.54       400
         SEDENTARY       0.74      0.84      0.78       400
VIGOROUS-INTENSITY       0.93      0.64      0.76       345

          accuracy                           0.60      1545
         macro avg       0.63      0.60      0.61      1545
      weighted avg       0.62      0.60      0.60      1545

2025-11-04 12:50:31.750058: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:50:31.761591: 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 00:00:1762257031.775013 1466044 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762257031.779197 1466044 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762257031.788869 1466044 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257031.788885 1466044 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257031.788887 1466044 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257031.788896 1466044 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:50:31.791993: 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 00:00:1762257034.149712 1466044 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762257035.845422 1466175 service.cc:152] XLA service 0x7e757c00c0e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762257035.845449 1466175 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:50:35.878741: 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 00:00:1762257036.049927 1466175 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762257038.274218 1466175 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 2/137

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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3891 - loss: 1.3178 - val_accuracy: 0.5079 - val_loss: 1.0872
Epoch 3/137

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[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4414 - loss: 1.2101
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.2053
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4427 - loss: 1.2034 - val_accuracy: 0.5365 - val_loss: 1.0396
Epoch 4/137

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[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1221
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1229
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Epoch 5/137

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Epoch 6/137

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Epoch 7/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5619 - loss: 0.9850 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5596 - loss: 0.9908
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5587 - loss: 0.9951
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Epoch 8/137

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[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5665 - loss: 0.9916
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5681 - loss: 0.9874
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5703 - loss: 0.9835
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Epoch 9/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5784 - loss: 0.9443 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5801 - loss: 0.9476
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5790 - loss: 0.9515
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5791 - loss: 0.9527
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5794 - loss: 0.9527 - val_accuracy: 0.6041 - val_loss: 0.8678
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 0.9834
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5603 - loss: 0.9586 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5709 - loss: 0.9462
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5760 - loss: 0.9400
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5781 - loss: 0.9384
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5789 - loss: 0.9380 - val_accuracy: 0.5953 - val_loss: 0.8683
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5156 - loss: 1.0624
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5879 - loss: 0.9198 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5898 - loss: 0.9217
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5900 - loss: 0.9208
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5904 - loss: 0.9203
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5908 - loss: 0.9197 - val_accuracy: 0.6130 - val_loss: 0.8520
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7344 - loss: 0.7875
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6016 - loss: 0.9102 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5986 - loss: 0.9131
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5984 - loss: 0.9116
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5984 - loss: 0.9098
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5988 - loss: 0.9086 - val_accuracy: 0.6097 - val_loss: 0.8427
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.8392
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6050 - loss: 0.8809 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6048 - loss: 0.8876
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6051 - loss: 0.8886
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6060 - loss: 0.8878
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6062 - loss: 0.8875 - val_accuracy: 0.6143 - val_loss: 0.8370
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 0.9145
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6011 - loss: 0.8888 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6033 - loss: 0.8845
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6052 - loss: 0.8833
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6069 - loss: 0.8823
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6073 - loss: 0.8823 - val_accuracy: 0.6087 - val_loss: 0.8345
Epoch 15/137

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[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6277 - loss: 0.8472
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Epoch 16/137

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Epoch 17/137

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Epoch 18/137

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[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6397 - loss: 0.8260 
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Epoch 19/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6269 - loss: 0.8184 
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6302 - loss: 0.8296 - val_accuracy: 0.6163 - val_loss: 0.8162
Epoch 20/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6372 - loss: 0.8215 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6353 - loss: 0.8234
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[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6339 - loss: 0.8223
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6337 - loss: 0.8221 - val_accuracy: 0.6104 - val_loss: 0.8242
Epoch 21/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6245 - loss: 0.8336 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6285 - loss: 0.8237
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[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6319 - loss: 0.8213
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6321 - loss: 0.8211 - val_accuracy: 0.6275 - val_loss: 0.8270
Epoch 22/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6219 - loss: 0.8236 
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Epoch 23/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6491 - loss: 0.7761 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6439 - loss: 0.7895
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6411 - loss: 0.7962
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6402 - loss: 0.7989
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6400 - loss: 0.7992 - val_accuracy: 0.6074 - val_loss: 0.8225
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7830
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6347 - loss: 0.7919 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6352 - loss: 0.7952
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6368 - loss: 0.7973
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6372 - loss: 0.7985
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6372 - loss: 0.7990 - val_accuracy: 0.6271 - val_loss: 0.8007
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.8419
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5960 - loss: 0.8374 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6090 - loss: 0.8240
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6153 - loss: 0.8177
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6194 - loss: 0.8146
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6211 - loss: 0.8133 - val_accuracy: 0.6124 - val_loss: 0.8289
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6562 - loss: 0.7171
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6369 - loss: 0.7848 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6430 - loss: 0.7802
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6441 - loss: 0.7809
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6443 - loss: 0.7824
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6443 - loss: 0.7828 - val_accuracy: 0.6212 - val_loss: 0.8163
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7344 - loss: 0.6073
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6391 - loss: 0.8070 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6439 - loss: 0.8018
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6450 - loss: 0.7987
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6461 - loss: 0.7952
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6464 - loss: 0.7941 - val_accuracy: 0.6084 - val_loss: 0.8290
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.6883
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6491 - loss: 0.7903 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6454 - loss: 0.7892
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6439 - loss: 0.7876
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6434 - loss: 0.7858
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6432 - loss: 0.7852 - val_accuracy: 0.6242 - val_loss: 0.8178
Epoch 29/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7188 - loss: 0.7306
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6683 - loss: 0.7690 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6605 - loss: 0.7713
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6579 - loss: 0.7754
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6560 - loss: 0.7778
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6553 - loss: 0.7784 - val_accuracy: 0.6087 - val_loss: 0.8188

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 706ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 23: 60.06 [%]
F1-score capturado en la ejecución 23: 60.58 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:46[0m 863ms/step
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 767us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.56 [%]
Global F1 score (validation) = 61.93 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.51091266 0.39390218 0.06229437 0.03289086]
 [0.48013076 0.3967143  0.04973454 0.07342049]
 [0.0611504  0.03922563 0.02680406 0.87281984]
 ...
 [0.02999753 0.01137424 0.95037127 0.00825698]
 [0.03988399 0.01554483 0.9317362  0.01283501]
 [0.0440066  0.01740305 0.9239309  0.01465945]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.67 [%]
Global accuracy score (test) = 57.8 [%]
Global F1 score (train) = 67.74 [%]
Global F1 score (test) = 58.16 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.39      0.38       400
MODERATE-INTENSITY       0.45      0.41      0.43       400
         SEDENTARY       0.69      0.89      0.77       400
VIGOROUS-INTENSITY       0.91      0.63      0.74       345

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

2025-11-04 12:51:02.936784: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:51:02.948267: 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 00:00:1762257062.961396 1469707 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762257062.965437 1469707 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762257062.975312 1469707 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257062.975328 1469707 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257062.975330 1469707 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257062.975331 1469707 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:51:02.978439: 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 00:00:1762257065.338057 1469707 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762257066.974166 1469813 service.cc:152] XLA service 0x71ed8400c680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762257066.974195 1469813 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:51:07.007301: 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 00:00:1762257067.177930 1469813 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762257069.395357 1469813 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:22[0m 3s/step - accuracy: 0.2500 - loss: 2.1477
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2868 - loss: 1.9172 
[1m 65/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2929 - loss: 1.8712
[1m102/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2983 - loss: 1.8228
[1m138/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3013 - loss: 1.7833
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3038 - loss: 1.7545
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3039 - loss: 1.7535 - val_accuracy: 0.4947 - val_loss: 1.1225
Epoch 2/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 1.4157
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3775 - loss: 1.3285 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3792 - loss: 1.3237
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3821 - loss: 1.3204
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.3149
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3863 - loss: 1.3123 - val_accuracy: 0.5243 - val_loss: 1.0771
Epoch 3/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4099 - loss: 1.2231 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4177 - loss: 1.2176
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4232 - loss: 1.2122
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4270 - loss: 1.2083
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Epoch 4/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.0914
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4317 - loss: 1.1821 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4410 - loss: 1.1718
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.1648
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4534 - loss: 1.1590
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Epoch 5/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0329
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0932 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0963
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.0974
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0973
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5020 - loss: 1.0973 - val_accuracy: 0.5647 - val_loss: 0.9816
Epoch 6/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3281 - loss: 1.2863
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.0993 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.0895
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0864
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0818
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5156 - loss: 1.0791 - val_accuracy: 0.5811 - val_loss: 0.9413
Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.0406
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5305 - loss: 1.0158 
[1m 69/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0150
[1m105/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5478 - loss: 1.0152
[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0167
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5486 - loss: 1.0174 - val_accuracy: 0.5989 - val_loss: 0.9007
Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8323
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5672 - loss: 0.9854 
[1m 69/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5630 - loss: 0.9949
[1m105/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5606 - loss: 1.0002
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5593 - loss: 1.0013
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5595 - loss: 1.0007 - val_accuracy: 0.5999 - val_loss: 0.8849
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0071
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5747 - loss: 0.9564 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5740 - loss: 0.9657
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5722 - loss: 0.9713
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5713 - loss: 0.9744
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5712 - loss: 0.9748 - val_accuracy: 0.6002 - val_loss: 0.8631
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0614
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5745 - loss: 0.9831 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5818 - loss: 0.9611
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5834 - loss: 0.9559
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5841 - loss: 0.9524
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5844 - loss: 0.9515 - val_accuracy: 0.6015 - val_loss: 0.8436
Epoch 11/137

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Epoch 12/137

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Epoch 13/137

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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5968 - loss: 0.9106
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Epoch 14/137

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[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6095 - loss: 0.8850
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6100 - loss: 0.8832
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Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8779
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6350 - loss: 0.8477 
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6185 - loss: 0.8638
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6154 - loss: 0.8657
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Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5781 - loss: 0.8558
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6021 - loss: 0.8692 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6054 - loss: 0.8683
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6076 - loss: 0.8658
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6095 - loss: 0.8634
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6098 - loss: 0.8629 - val_accuracy: 0.5976 - val_loss: 0.8113
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9785
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6062 - loss: 0.8895 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6075 - loss: 0.8846
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6091 - loss: 0.8777
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6108 - loss: 0.8724
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6117 - loss: 0.8701 - val_accuracy: 0.6353 - val_loss: 0.8085
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6875 - loss: 0.8095
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6425 - loss: 0.8284 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6356 - loss: 0.8402
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6340 - loss: 0.8419
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6332 - loss: 0.8413
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Epoch 19/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6142 - loss: 0.8416 
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6270 - loss: 0.8264 - val_accuracy: 0.6110 - val_loss: 0.8172
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.7031 - loss: 0.6987
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6425 - loss: 0.8106 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6376 - loss: 0.8195
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6350 - loss: 0.8229
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6332 - loss: 0.8251
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6329 - loss: 0.8260 - val_accuracy: 0.6107 - val_loss: 0.8163
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6250 - loss: 0.7696
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6450 - loss: 0.8000 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6439 - loss: 0.8063
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6414 - loss: 0.8119
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6399 - loss: 0.8144
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6394 - loss: 0.8149 - val_accuracy: 0.6097 - val_loss: 0.8076
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6719 - loss: 0.7662
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6367 - loss: 0.7923 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6294 - loss: 0.8013
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6278 - loss: 0.8061
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Epoch 23/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6316 - loss: 0.8500 
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6390 - loss: 0.8167 - val_accuracy: 0.6344 - val_loss: 0.8053

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 831ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 24: 57.8 [%]
F1-score capturado en la ejecución 24: 58.16 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 739us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 62.88 [%]
Global F1 score (validation) = 61.91 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.43409047 0.50342554 0.01833761 0.04414634]
 [0.42340243 0.5646034  0.00434415 0.00764999]
 [0.44902995 0.44334775 0.04199957 0.06562271]
 ...
 [0.02559754 0.00955269 0.95555055 0.00929922]
 [0.07569496 0.03635251 0.86675745 0.02119508]
 [0.02723284 0.01031347 0.952423   0.0100308 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.84 [%]
Global accuracy score (test) = 61.1 [%]
Global F1 score (train) = 65.62 [%]
Global F1 score (test) = 60.35 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.27      0.33       400
MODERATE-INTENSITY       0.49      0.65      0.56       400
         SEDENTARY       0.71      0.90      0.79       400
VIGOROUS-INTENSITY       0.90      0.63      0.74       345

          accuracy                           0.61      1545
         macro avg       0.63      0.61      0.60      1545
      weighted avg       0.62      0.61      0.60      1545

2025-11-04 12:51:32.055275: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:51:32.066431: 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 00:00:1762257092.079756 1472784 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762257092.083729 1472784 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762257092.093834 1472784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257092.093854 1472784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257092.093855 1472784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257092.093856 1472784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:51:32.096847: 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 00:00:1762257094.443226 1472784 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762257096.088390 1472914 service.cc:152] XLA service 0x72577c00d2a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762257096.088436 1472914 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:51:36.124925: 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 00:00:1762257096.296098 1472914 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762257098.531688 1472914 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:28[0m 3s/step - accuracy: 0.2344 - loss: 2.2806
[1m 31/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2876 - loss: 2.1280 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2935 - loss: 2.0154
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2981 - loss: 1.9463
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3025 - loss: 1.8869
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.3047 - loss: 1.8625
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.3049 - loss: 1.8613 - val_accuracy: 0.4428 - val_loss: 1.1513
Epoch 2/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3825 - loss: 1.3801 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3753 - loss: 1.3709
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3771 - loss: 1.3599
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3797 - loss: 1.3492
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3814 - loss: 1.3433 - val_accuracy: 0.5151 - val_loss: 1.0860
Epoch 3/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3940 - loss: 1.2556 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4093 - loss: 1.2377
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.2294
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4223 - loss: 1.2219
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4248 - loss: 1.2183 - val_accuracy: 0.5378 - val_loss: 1.0417
Epoch 4/137

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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1454
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1424
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1385
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4688 - loss: 1.1369 - val_accuracy: 0.5598 - val_loss: 1.0092
Epoch 5/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0859 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0859
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0827
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Epoch 6/137

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[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0307
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0314
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0329
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Epoch 7/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0085 
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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5501 - loss: 1.0041
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5509 - loss: 1.0027 - val_accuracy: 0.5979 - val_loss: 0.8919
Epoch 8/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5780 - loss: 0.9503
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5764 - loss: 0.9540
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5761 - loss: 0.9549 - val_accuracy: 0.6038 - val_loss: 0.8719
Epoch 9/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5912 - loss: 0.9289 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5908 - loss: 0.9321
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5907 - loss: 0.9321
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5902 - loss: 0.9320
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5900 - loss: 0.9321 - val_accuracy: 0.6094 - val_loss: 0.8603
Epoch 10/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5927 - loss: 0.9310 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5936 - loss: 0.9301
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5941 - loss: 0.9292
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5941 - loss: 0.9287
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5940 - loss: 0.9285 - val_accuracy: 0.5995 - val_loss: 0.8472
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9786
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5802 - loss: 0.9006 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5907 - loss: 0.8986
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5959 - loss: 0.8981
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5975 - loss: 0.8974
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5976 - loss: 0.8974 - val_accuracy: 0.6150 - val_loss: 0.8379
Epoch 12/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6213 - loss: 0.8798 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6211 - loss: 0.8787
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6188 - loss: 0.8797
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6169 - loss: 0.8811
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Epoch 13/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6048 - loss: 0.8895 
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Epoch 14/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6141 - loss: 0.8603 
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[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6155 - loss: 0.8604
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6148 - loss: 0.8620
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Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.8473
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[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6199 - loss: 0.8570
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6167 - loss: 0.8591
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Epoch 16/137

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[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6084 - loss: 0.8563 
[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6107 - loss: 0.8560
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6129 - loss: 0.8561
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6142 - loss: 0.8570
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6148 - loss: 0.8573 - val_accuracy: 0.6199 - val_loss: 0.8147
Epoch 17/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.8442
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6211 - loss: 0.8110 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6245 - loss: 0.8202
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6222 - loss: 0.8279
[1m140/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6217 - loss: 0.8316
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6216 - loss: 0.8332 - val_accuracy: 0.6153 - val_loss: 0.8192
Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.7959
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6158 - loss: 0.8182 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6179 - loss: 0.8219
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6202 - loss: 0.8217
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6216 - loss: 0.8231
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6219 - loss: 0.8236 - val_accuracy: 0.6061 - val_loss: 0.8337
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.7935
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6152 - loss: 0.8240 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6169 - loss: 0.8233
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6193 - loss: 0.8224
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6208 - loss: 0.8215
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6212 - loss: 0.8212 - val_accuracy: 0.6133 - val_loss: 0.8182
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9285
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6284 - loss: 0.8214 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6248 - loss: 0.8251
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6247 - loss: 0.8251
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6252 - loss: 0.8246
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6259 - loss: 0.8244 - val_accuracy: 0.6176 - val_loss: 0.8189
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9161
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6060 - loss: 0.8471 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6145 - loss: 0.8345
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6182 - loss: 0.8290
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6211 - loss: 0.8242
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Epoch 22/137

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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6299 - loss: 0.8020
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6308 - loss: 0.8041
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Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7244
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[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6428 - loss: 0.7956
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6409 - loss: 0.7964
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.7972
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Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8456
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6458 - loss: 0.7887 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6435 - loss: 0.7950
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6420 - loss: 0.7953
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6402 - loss: 0.7966
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6398 - loss: 0.7969 - val_accuracy: 0.6252 - val_loss: 0.8220
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8149
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6351 - loss: 0.8319 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.8174
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6412 - loss: 0.8105
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6423 - loss: 0.8060
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6424 - loss: 0.8051 - val_accuracy: 0.6183 - val_loss: 0.8127
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8405
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6334 - loss: 0.8259 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6338 - loss: 0.8139
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6359 - loss: 0.8051
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6374 - loss: 0.7992
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6378 - loss: 0.7974 - val_accuracy: 0.6193 - val_loss: 0.8094
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 0.9375
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6303 - loss: 0.7915 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6369 - loss: 0.7851
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6395 - loss: 0.7814
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6398 - loss: 0.7818
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6398 - loss: 0.7819 - val_accuracy: 0.6239 - val_loss: 0.8091
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.6989
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6660 - loss: 0.7340 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6595 - loss: 0.7483
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6569 - loss: 0.7547
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6557 - loss: 0.7586
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6555 - loss: 0.7594 - val_accuracy: 0.6176 - val_loss: 0.8163
Epoch 29/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6719 - loss: 0.7529
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6595 - loss: 0.7553 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6533 - loss: 0.7645
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6513 - loss: 0.7675
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6496 - loss: 0.7699
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6493 - loss: 0.7703 - val_accuracy: 0.6163 - val_loss: 0.8066
Epoch 30/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6875 - loss: 0.6726
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6632 - loss: 0.7422 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6505 - loss: 0.7561
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6475 - loss: 0.7607
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6465 - loss: 0.7642 - val_accuracy: 0.6281 - val_loss: 0.8125
Epoch 31/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7312
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6574 - loss: 0.7834 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6551 - loss: 0.7824
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6550 - loss: 0.7779
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6544 - loss: 0.7763
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6538 - loss: 0.7757 - val_accuracy: 0.6242 - val_loss: 0.8130
Epoch 32/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7188 - loss: 0.6679
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6560 - loss: 0.7585 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6531 - loss: 0.7668
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6524 - loss: 0.7673
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6523 - loss: 0.7667
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6522 - loss: 0.7667 - val_accuracy: 0.6196 - val_loss: 0.8079
Epoch 33/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6094 - loss: 0.7245
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6596 - loss: 0.7354 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6568 - loss: 0.7475
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Epoch 34/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6562 - loss: 0.7265 
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6530 - loss: 0.7419 - val_accuracy: 0.6133 - val_loss: 0.8221

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 685ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 25: 61.1 [%]
F1-score capturado en la ejecución 25: 60.35 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[1m74/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 692us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.76 [%]
Global F1 score (validation) = 62.1 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.47976086 0.46436223 0.02735594 0.02852099]
 [0.49414298 0.46734327 0.01998429 0.01852944]
 [0.21061052 0.11722535 0.45818162 0.21398255]
 ...
 [0.06795941 0.03188602 0.86287576 0.03727886]
 [0.04493656 0.01993684 0.9155274  0.01959909]
 [0.0441509  0.01952118 0.9169101  0.01941778]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.41 [%]
Global accuracy score (test) = 59.29 [%]
Global F1 score (train) = 67.84 [%]
Global F1 score (test) = 59.14 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.32      0.34       400
MODERATE-INTENSITY       0.49      0.56      0.52       400
         SEDENTARY       0.72      0.88      0.79       400
VIGOROUS-INTENSITY       0.83      0.62      0.71       345

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

2025-11-04 12:52:05.034526: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:52:05.045767: 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 00:00:1762257125.058838 1476890 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762257125.062960 1476890 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762257125.072720 1476890 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257125.072734 1476890 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257125.072736 1476890 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257125.072737 1476890 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:52:05.075826: 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 00:00:1762257127.417085 1476890 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762257129.070372 1477007 service.cc:152] XLA service 0x7317dc0028b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762257129.070400 1477007 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:52:09.105230: 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 00:00:1762257129.275691 1477007 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762257131.479183 1477007 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:23[0m 3s/step - accuracy: 0.2344 - loss: 2.4700
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2613 - loss: 2.1486 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2751 - loss: 2.0358
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2808 - loss: 1.9752
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2859 - loss: 1.9293
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2898 - loss: 1.8987
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2899 - loss: 1.8975 - val_accuracy: 0.4826 - val_loss: 1.1335
Epoch 2/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.3081
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3791 - loss: 1.3870 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3766 - loss: 1.3841
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3770 - loss: 1.3743
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3790 - loss: 1.3641
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3795 - loss: 1.3619 - val_accuracy: 0.5302 - val_loss: 1.0813
Epoch 3/137

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[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4097 - loss: 1.2433 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.2332
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4201 - loss: 1.2278
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4218 - loss: 1.2226
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4222 - loss: 1.2215 - val_accuracy: 0.5223 - val_loss: 1.0493
Epoch 4/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4150 - loss: 1.1947 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.1836
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[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.1757
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Epoch 5/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1445 
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[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1298
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Epoch 6/137

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[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1035
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.0966
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.0905
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Epoch 7/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0394 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0397
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[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0438
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Epoch 8/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0384
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0244 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0221
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0204
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0187
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5407 - loss: 1.0178 - val_accuracy: 0.5831 - val_loss: 0.9050
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1482
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0340 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5448 - loss: 1.0175
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5500 - loss: 1.0067
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5523 - loss: 1.0023
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5529 - loss: 1.0010 - val_accuracy: 0.6022 - val_loss: 0.8903
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9477
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5918 - loss: 0.9493 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5832 - loss: 0.9593
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5813 - loss: 0.9611
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5808 - loss: 0.9607
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5808 - loss: 0.9606 - val_accuracy: 0.5825 - val_loss: 0.8652
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 1.1231
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6020 - loss: 0.9386 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5967 - loss: 0.9367
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5951 - loss: 0.9351
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5935 - loss: 0.9356
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5926 - loss: 0.9357 - val_accuracy: 0.5986 - val_loss: 0.8614
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.8906
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5854 - loss: 0.9152 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5880 - loss: 0.9148
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[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5890 - loss: 0.9169
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Epoch 13/137

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Epoch 14/137

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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6037 - loss: 0.8915
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[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6020 - loss: 0.8922
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Epoch 15/137

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[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6131 - loss: 0.8739
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[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6127 - loss: 0.8758
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Epoch 16/137

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[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6272 - loss: 0.8560
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6267 - loss: 0.8572 - val_accuracy: 0.6153 - val_loss: 0.8418
Epoch 17/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6245 - loss: 0.8580 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6285 - loss: 0.8574
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6283 - loss: 0.8572
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6273 - loss: 0.8572
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6265 - loss: 0.8574 - val_accuracy: 0.5992 - val_loss: 0.8382
Epoch 18/137

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[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6435 - loss: 0.8097 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6365 - loss: 0.8239
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6316 - loss: 0.8322
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6284 - loss: 0.8382
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6275 - loss: 0.8399 - val_accuracy: 0.6189 - val_loss: 0.8267
Epoch 19/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6192 - loss: 0.8505 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6205 - loss: 0.8495
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6215 - loss: 0.8483
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6219 - loss: 0.8478
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6220 - loss: 0.8476 - val_accuracy: 0.6179 - val_loss: 0.8208
Epoch 20/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6511 - loss: 0.8145 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6395 - loss: 0.8261
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[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6333 - loss: 0.8333
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Epoch 21/137

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[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6200 - loss: 0.8270
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6204 - loss: 0.8290
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6213 - loss: 0.8296
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6214 - loss: 0.8299 - val_accuracy: 0.6114 - val_loss: 0.8256
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.7500 - loss: 0.7713
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6474 - loss: 0.8162 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6421 - loss: 0.8174
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6393 - loss: 0.8190
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6380 - loss: 0.8210
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6377 - loss: 0.8217 - val_accuracy: 0.6147 - val_loss: 0.8227
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7477
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6442 - loss: 0.8036 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6363 - loss: 0.8133
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6333 - loss: 0.8159
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6320 - loss: 0.8168
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6314 - loss: 0.8174 - val_accuracy: 0.6097 - val_loss: 0.8454
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 0.8207
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6211 - loss: 0.8009 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6274 - loss: 0.8073
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6304 - loss: 0.8107
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6318 - loss: 0.8123
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6320 - loss: 0.8127 - val_accuracy: 0.6173 - val_loss: 0.8232

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 835ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 26: 59.29 [%]
F1-score capturado en la ejecución 26: 59.14 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:48[0m 869ms/step
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 713us/step  
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 710us/step
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 699us/step
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 703us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 758us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 61.2 [%]
Global F1 score (validation) = 61.52 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.06246657 0.07556584 0.00342078 0.8585468 ]
 [0.40529972 0.3461675  0.0738701  0.17466274]
 [0.47214937 0.45861486 0.01840891 0.05082697]
 ...
 [0.05377143 0.02379349 0.91678387 0.0056512 ]
 [0.0208899  0.0079991  0.96561617 0.00549484]
 [0.01958184 0.0073226  0.96733445 0.00576115]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.38 [%]
Global accuracy score (test) = 58.9 [%]
Global F1 score (train) = 67.09 [%]
Global F1 score (test) = 59.39 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.36      0.37       400
MODERATE-INTENSITY       0.47      0.52      0.49       400
         SEDENTARY       0.73      0.85      0.79       400
VIGOROUS-INTENSITY       0.87      0.63      0.73       345

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

2025-11-04 12:52:34.440884: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:52:34.452751: 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 00:00:1762257154.466333 1480082 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762257154.470461 1480082 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762257154.480151 1480082 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257154.480167 1480082 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257154.480168 1480082 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257154.480170 1480082 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:52:34.483247: 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 00:00:1762257156.822659 1480082 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762257158.481777 1480185 service.cc:152] XLA service 0x7e18e8005830 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762257158.481824 1480185 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:52:38.524288: 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 00:00:1762257158.689861 1480185 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762257160.914387 1480185 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:27[0m 3s/step - accuracy: 0.1250 - loss: 2.4650
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Epoch 2/137

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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3690 - loss: 1.3654
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3714 - loss: 1.3601 - val_accuracy: 0.5039 - val_loss: 1.1038
Epoch 3/137

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[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4276 - loss: 1.2143
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4281 - loss: 1.2129
[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2107
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4300 - loss: 1.2086 - val_accuracy: 0.5338 - val_loss: 1.0502
Epoch 4/137

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[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4621 - loss: 1.1639
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1602
[1m141/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1580
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4661 - loss: 1.1567 - val_accuracy: 0.5473 - val_loss: 1.0242
Epoch 5/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1326 
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[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1110
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4805 - loss: 1.1088
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Epoch 6/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0788 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0795
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0778
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0764
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Epoch 7/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0661 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0587
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0555
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0523
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Epoch 8/137

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[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5494 - loss: 1.0399 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0353
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0292
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0244
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5452 - loss: 1.0233 - val_accuracy: 0.6124 - val_loss: 0.8782
Epoch 9/137

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[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5605 - loss: 0.9522 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5687 - loss: 0.9581
[1m110/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5718 - loss: 0.9597
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5730 - loss: 0.9609
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5732 - loss: 0.9615 - val_accuracy: 0.5963 - val_loss: 0.8783
Epoch 10/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5764 - loss: 0.9608 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5808 - loss: 0.9568
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5830 - loss: 0.9539
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5832 - loss: 0.9521
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5831 - loss: 0.9516 - val_accuracy: 0.6245 - val_loss: 0.8369
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 0.8864
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5769 - loss: 0.9432 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5802 - loss: 0.9383
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5812 - loss: 0.9371
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5816 - loss: 0.9369
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5820 - loss: 0.9366 - val_accuracy: 0.6147 - val_loss: 0.8542
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.7863
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6043 - loss: 0.9042 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6039 - loss: 0.9111
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6034 - loss: 0.9134
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6029 - loss: 0.9145
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6025 - loss: 0.9147 - val_accuracy: 0.6058 - val_loss: 0.8303
Epoch 13/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6051 - loss: 0.8956 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6029 - loss: 0.8976
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6039 - loss: 0.8938
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6033 - loss: 0.8937
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6030 - loss: 0.8940 - val_accuracy: 0.6156 - val_loss: 0.8181
Epoch 14/137

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[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6076 - loss: 0.9004 
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Epoch 15/137

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[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6113 - loss: 0.8840
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6122 - loss: 0.8772
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Epoch 16/137

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[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6268 - loss: 0.8573
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Epoch 17/137

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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6205 - loss: 0.8371
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6204 - loss: 0.8396
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6212 - loss: 0.8417
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Epoch 18/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.6959
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6335 - loss: 0.8324 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6262 - loss: 0.8408
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6249 - loss: 0.8419
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6248 - loss: 0.8425
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6248 - loss: 0.8429 - val_accuracy: 0.6347 - val_loss: 0.7833
Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6094 - loss: 0.8852
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6196 - loss: 0.8172 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6209 - loss: 0.8250
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6207 - loss: 0.8282
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6203 - loss: 0.8310
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6202 - loss: 0.8315 - val_accuracy: 0.6311 - val_loss: 0.8000
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.7635
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6148 - loss: 0.8400 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6162 - loss: 0.8459
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6168 - loss: 0.8434
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6175 - loss: 0.8399
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6178 - loss: 0.8385 - val_accuracy: 0.6163 - val_loss: 0.8126
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.8613
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6072 - loss: 0.8241 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6089 - loss: 0.8335
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6120 - loss: 0.8341
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6147 - loss: 0.8336
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6156 - loss: 0.8334 - val_accuracy: 0.6255 - val_loss: 0.8151
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6875 - loss: 0.7744
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6501 - loss: 0.8078 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6418 - loss: 0.8116
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[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6355 - loss: 0.8179
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Epoch 23/137

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[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6388 - loss: 0.8040
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 829ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 27: 58.9 [%]
F1-score capturado en la ejecución 27: 59.39 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:40[0m 846ms/step
[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 782us/step  
[1m134/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 755us/step
[1m205/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 740us/step
[1m276/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 733us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 771us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.32 [%]
Global F1 score (validation) = 62.1 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.43964994 0.5000359  0.01657411 0.04374013]
 [0.39595777 0.2476289  0.23075138 0.12566191]
 [0.47387707 0.42957947 0.04635163 0.05019183]
 ...
 [0.0301256  0.01184669 0.9483308  0.00969685]
 [0.0251423  0.00945962 0.9540251  0.01137296]
 [0.02050391 0.00743812 0.9635619  0.00849605]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 66.76 [%]
Global accuracy score (test) = 58.38 [%]
Global F1 score (train) = 65.59 [%]
Global F1 score (test) = 58.4 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.29      0.32       400
MODERATE-INTENSITY       0.46      0.53      0.49       400
         SEDENTARY       0.68      0.89      0.77       400
VIGOROUS-INTENSITY       0.92      0.63      0.75       345

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

2025-11-04 12:53:03.555040: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:53:03.566494: 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 00:00:1762257183.579430 1483156 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762257183.583475 1483156 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762257183.593434 1483156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257183.593449 1483156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257183.593451 1483156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257183.593452 1483156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:53:03.596736: 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 00:00:1762257185.953369 1483156 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762257187.626800 1483290 service.cc:152] XLA service 0x7277ac00c510 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762257187.626824 1483290 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:53:07.660694: 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 00:00:1762257187.831705 1483290 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762257190.078353 1483290 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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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2885 - loss: 1.8256
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2886 - loss: 1.8244 - val_accuracy: 0.4944 - val_loss: 1.1362
Epoch 2/137

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[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3708 - loss: 1.3425
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3763 - loss: 1.3303
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3795 - loss: 1.3218
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3805 - loss: 1.3195 - val_accuracy: 0.5174 - val_loss: 1.0824
Epoch 3/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1283
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[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4494 - loss: 1.1999
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1991
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4485 - loss: 1.1980
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Epoch 4/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1476
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1426
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Epoch 5/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0845
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.0788 
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[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.0881
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.0863
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5038 - loss: 1.0851 - val_accuracy: 0.5785 - val_loss: 0.9593
Epoch 6/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5523 - loss: 1.0305 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0440
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0493
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0498
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5320 - loss: 1.0498 - val_accuracy: 0.5844 - val_loss: 0.9451
Epoch 7/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0615 
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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0384
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0327
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Epoch 8/137

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Epoch 9/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5912 - loss: 0.9486 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5894 - loss: 0.9456
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5886 - loss: 0.9445
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5884 - loss: 0.9457
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Epoch 10/137

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[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5925 - loss: 0.9432
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Epoch 11/137

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[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5871 - loss: 0.9320
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5892 - loss: 0.9294
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5900 - loss: 0.9284 - val_accuracy: 0.5982 - val_loss: 0.8554
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8673
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5926 - loss: 0.9136 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5943 - loss: 0.9121
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5949 - loss: 0.9095
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5959 - loss: 0.9060
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5962 - loss: 0.9050 - val_accuracy: 0.6028 - val_loss: 0.8346
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9660
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6130 - loss: 0.8806 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6088 - loss: 0.8821
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6100 - loss: 0.8807
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6105 - loss: 0.8812
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6105 - loss: 0.8817 - val_accuracy: 0.6074 - val_loss: 0.8307
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.9117
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6046 - loss: 0.8924 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6048 - loss: 0.8878
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6075 - loss: 0.8833
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6093 - loss: 0.8805
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6097 - loss: 0.8800 - val_accuracy: 0.6186 - val_loss: 0.8156
Epoch 15/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 0.8979
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6084 - loss: 0.8530 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6063 - loss: 0.8622
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6060 - loss: 0.8661
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6070 - loss: 0.8669
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6075 - loss: 0.8668 - val_accuracy: 0.6193 - val_loss: 0.8114
Epoch 16/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8187
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6267 - loss: 0.8172 
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[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6206 - loss: 0.8363
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Epoch 17/137

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Epoch 18/137

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[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6206 - loss: 0.8543
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Epoch 19/137

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[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6351 - loss: 0.8342
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[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6343 - loss: 0.8276
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Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.7742
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6174 - loss: 0.8332 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6172 - loss: 0.8308
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6179 - loss: 0.8296
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6196 - loss: 0.8284
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Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9040
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6362 - loss: 0.8158 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6341 - loss: 0.8187
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6348 - loss: 0.8173
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6348 - loss: 0.8171
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6345 - loss: 0.8172 - val_accuracy: 0.6137 - val_loss: 0.8139
Epoch 22/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8146
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6377 - loss: 0.7948 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6399 - loss: 0.8021
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6401 - loss: 0.8046
[1m141/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6394 - loss: 0.8071
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6392 - loss: 0.8080 - val_accuracy: 0.6061 - val_loss: 0.8029
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.8564
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6179 - loss: 0.8307 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6254 - loss: 0.8232
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6275 - loss: 0.8204
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6301 - loss: 0.8170
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6306 - loss: 0.8164 - val_accuracy: 0.6071 - val_loss: 0.7971
Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 0.9821
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6311 - loss: 0.7968 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6347 - loss: 0.7979
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6356 - loss: 0.8001
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6364 - loss: 0.8017
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Epoch 25/137

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[1m 31/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6546 - loss: 0.7474 
[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6505 - loss: 0.7576
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6508 - loss: 0.7617
[1m145/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6503 - loss: 0.7656
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6497 - loss: 0.7679 - val_accuracy: 0.6015 - val_loss: 0.8154
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.7031 - loss: 0.8024
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6629 - loss: 0.7653 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6526 - loss: 0.7777
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6484 - loss: 0.7838
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6469 - loss: 0.7870
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6466 - loss: 0.7876 - val_accuracy: 0.6087 - val_loss: 0.8294
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.7500 - loss: 0.6289
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6510 - loss: 0.7699 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6443 - loss: 0.7799
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6419 - loss: 0.7845
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6405 - loss: 0.7869
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6401 - loss: 0.7874 - val_accuracy: 0.6061 - val_loss: 0.8226
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.8337
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6376 - loss: 0.7961 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6419 - loss: 0.7897
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6440 - loss: 0.7873
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6450 - loss: 0.7862
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6454 - loss: 0.7857 - val_accuracy: 0.6068 - val_loss: 0.8258

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 829ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 58.38 [%]
F1-score capturado en la ejecución 28: 58.4 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:49[0m 871ms/step
[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 783us/step  
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 717us/step
[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 723us/step
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 714us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m61/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 840us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 61.5 [%]
Global F1 score (validation) = 62.07 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.45777342 0.28426707 0.20225061 0.05570892]
 [0.43552855 0.4988251  0.0026398  0.06300654]
 [0.14299434 0.11583585 0.02027931 0.7208905 ]
 ...
 [0.07020447 0.03622109 0.8647894  0.02878501]
 [0.11047292 0.06138765 0.77650565 0.05163377]
 [0.0539254  0.02682868 0.9009273  0.01831858]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.86 [%]
Global accuracy score (test) = 58.77 [%]
Global F1 score (train) = 67.83 [%]
Global F1 score (test) = 59.77 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.38      0.38       400
MODERATE-INTENSITY       0.46      0.51      0.48       400
         SEDENTARY       0.75      0.83      0.79       400
VIGOROUS-INTENSITY       0.89      0.64      0.75       345

          accuracy                           0.59      1545
         macro avg       0.62      0.59      0.60      1545
      weighted avg       0.61      0.59      0.59      1545

2025-11-04 12:53:34.557338: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-04 12:53:34.568493: 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 00:00:1762257214.581421 1486700 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762257214.585518 1486700 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762257214.595332 1486700 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257214.595348 1486700 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257214.595350 1486700 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762257214.595359 1486700 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 12:53:34.598492: 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 00:00:1762257216.945198 1486700 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/137
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762257218.575740 1486830 service.cc:152] XLA service 0x74b16000cca0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762257218.575776 1486830 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 12:53:38.612725: 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 00:00:1762257218.787948 1486830 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762257221.016721 1486830 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:26[0m 3s/step - accuracy: 0.2031 - loss: 2.3189
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[1m144/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2961 - loss: 1.8136
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2998 - loss: 1.7880
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.2999 - loss: 1.7870 - val_accuracy: 0.4662 - val_loss: 1.1339
Epoch 2/137

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[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3881 - loss: 1.3375
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3907 - loss: 1.3287
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3935 - loss: 1.3206
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3940 - loss: 1.3194 - val_accuracy: 0.5204 - val_loss: 1.0829
Epoch 3/137

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[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4295 - loss: 1.2141 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2121
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4335 - loss: 1.2048
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4363 - loss: 1.1996
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4367 - loss: 1.1987 - val_accuracy: 0.5338 - val_loss: 1.0424
Epoch 4/137

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[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1443 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1463
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1472
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1456
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4661 - loss: 1.1444 - val_accuracy: 0.5598 - val_loss: 1.0135
Epoch 5/137

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[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4938 - loss: 1.1036 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1027
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1032
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1029
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Epoch 6/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.0763 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0704
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0690
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0667
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Epoch 7/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1891
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5443 - loss: 1.0440 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0387
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0359
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0333
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5404 - loss: 1.0323 - val_accuracy: 0.6032 - val_loss: 0.9087
Epoch 8/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5756 - loss: 0.9714 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5734 - loss: 0.9802
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5724 - loss: 0.9819
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5715 - loss: 0.9820
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5715 - loss: 0.9816 - val_accuracy: 0.6140 - val_loss: 0.8772
Epoch 9/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6719 - loss: 0.8118
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5923 - loss: 0.9474 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5839 - loss: 0.9526
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5808 - loss: 0.9566
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5790 - loss: 0.9584
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5789 - loss: 0.9585 - val_accuracy: 0.6193 - val_loss: 0.8698
Epoch 10/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0308
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5913 - loss: 0.9280 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5934 - loss: 0.9301
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5923 - loss: 0.9333
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5915 - loss: 0.9343
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5913 - loss: 0.9343 - val_accuracy: 0.6140 - val_loss: 0.8567
Epoch 11/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.8590
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5958 - loss: 0.8952 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5979 - loss: 0.9042
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5995 - loss: 0.9075
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6007 - loss: 0.9081
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6008 - loss: 0.9085 - val_accuracy: 0.6114 - val_loss: 0.8420
Epoch 12/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.8984
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6000 - loss: 0.8902 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6020 - loss: 0.8949
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6025 - loss: 0.8974
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6028 - loss: 0.8985
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6025 - loss: 0.8992 - val_accuracy: 0.6124 - val_loss: 0.8437
Epoch 13/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9086
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6045 - loss: 0.8898 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6049 - loss: 0.8943
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6052 - loss: 0.8965
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6057 - loss: 0.8958
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6058 - loss: 0.8953 - val_accuracy: 0.6051 - val_loss: 0.8384
Epoch 14/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.6250 - loss: 0.8747
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[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6046 - loss: 0.8659
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6066 - loss: 0.8665
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6072 - loss: 0.8685
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Epoch 15/137

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[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6078 - loss: 0.8780
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6096 - loss: 0.8759
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Epoch 16/137

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[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6209 - loss: 0.8586
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Epoch 17/137

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[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6235 - loss: 0.8452 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6251 - loss: 0.8454
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6272 - loss: 0.8438
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6277 - loss: 0.8440
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Epoch 18/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6233 - loss: 0.8241 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6283 - loss: 0.8234
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6299 - loss: 0.8243
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6292 - loss: 0.8277
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Epoch 19/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.8238
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6334 - loss: 0.8058 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6350 - loss: 0.8158
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6346 - loss: 0.8237
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6344 - loss: 0.8267
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6347 - loss: 0.8270 - val_accuracy: 0.6239 - val_loss: 0.8208
Epoch 20/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.7607
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6058 - loss: 0.8280 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6174 - loss: 0.8321
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6226 - loss: 0.8310
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6254 - loss: 0.8298
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6259 - loss: 0.8297 - val_accuracy: 0.6081 - val_loss: 0.8222
Epoch 21/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.8627
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6443 - loss: 0.8250 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6462 - loss: 0.8181
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6459 - loss: 0.8151
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6442 - loss: 0.8150
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Epoch 22/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6524 - loss: 0.7893 
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[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6413 - loss: 0.7994 - val_accuracy: 0.6041 - val_loss: 0.8272
Epoch 23/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7188 - loss: 0.5921
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6431 - loss: 0.7686 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6431 - loss: 0.7788
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6426 - loss: 0.7844
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6423 - loss: 0.7893
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Epoch 24/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.7031 - loss: 0.7966
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6228 - loss: 0.8085 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6271 - loss: 0.8045
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6301 - loss: 0.8017
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6318 - loss: 0.8013
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6323 - loss: 0.8012 - val_accuracy: 0.6163 - val_loss: 0.8232
Epoch 25/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.8798
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6492 - loss: 0.7741 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6453 - loss: 0.7835
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6440 - loss: 0.7868
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6440 - loss: 0.7874
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6438 - loss: 0.7879 - val_accuracy: 0.6219 - val_loss: 0.8186
Epoch 26/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7849
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6493 - loss: 0.7885 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6471 - loss: 0.7932
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6474 - loss: 0.7911
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6479 - loss: 0.7895
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6480 - loss: 0.7893 - val_accuracy: 0.6239 - val_loss: 0.8189
Epoch 27/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.7031 - loss: 0.7101
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6535 - loss: 0.7812 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6506 - loss: 0.7825
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6500 - loss: 0.7829
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6500 - loss: 0.7840
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6501 - loss: 0.7843 - val_accuracy: 0.6232 - val_loss: 0.8232
Epoch 28/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7344 - loss: 0.7858
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6557 - loss: 0.7845 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6531 - loss: 0.7800
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6512 - loss: 0.7813
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6503 - loss: 0.7828
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6501 - loss: 0.7831 - val_accuracy: 0.6212 - val_loss: 0.8190
Epoch 29/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6719 - loss: 0.7916
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6460 - loss: 0.7701 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6538 - loss: 0.7664
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6551 - loss: 0.7678
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6556 - loss: 0.7683
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6557 - loss: 0.7688 - val_accuracy: 0.6110 - val_loss: 0.8270
Epoch 30/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7500 - loss: 0.6194
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6483 - loss: 0.7872 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6499 - loss: 0.7784
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6512 - loss: 0.7728
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6522 - loss: 0.7704
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6524 - loss: 0.7700 - val_accuracy: 0.6156 - val_loss: 0.8185
Epoch 31/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6484 - loss: 0.7504 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6540 - loss: 0.7594
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Epoch 32/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6597 - loss: 0.8023 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6603 - loss: 0.7885
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[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6600 - loss: 0.7780
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Epoch 33/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6838 - loss: 0.7344 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6799 - loss: 0.7446
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[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6745 - loss: 0.7530
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6739 - loss: 0.7536 - val_accuracy: 0.6097 - val_loss: 0.8178
Epoch 34/137

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[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6321 - loss: 0.8040 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6415 - loss: 0.7889
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6462 - loss: 0.7831
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6492 - loss: 0.7787
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6497 - loss: 0.7779 - val_accuracy: 0.6143 - val_loss: 0.8191
Epoch 35/137

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[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6606 - loss: 0.7450 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6582 - loss: 0.7485
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6578 - loss: 0.7517
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6584 - loss: 0.7524
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6583 - loss: 0.7530 - val_accuracy: 0.6140 - val_loss: 0.8006
Epoch 36/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.7838
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6460 - loss: 0.7725 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6513 - loss: 0.7680
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6533 - loss: 0.7633
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6540 - loss: 0.7609
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6541 - loss: 0.7607 - val_accuracy: 0.6294 - val_loss: 0.8052
Epoch 37/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7415
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6594 - loss: 0.7592 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6607 - loss: 0.7552
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6613 - loss: 0.7539
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6622 - loss: 0.7534
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6624 - loss: 0.7535 - val_accuracy: 0.6173 - val_loss: 0.8224
Epoch 38/137

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[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6550 - loss: 0.7705 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6596 - loss: 0.7655
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6621 - loss: 0.7610
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6638 - loss: 0.7583
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6643 - loss: 0.7574 - val_accuracy: 0.6245 - val_loss: 0.8148
Epoch 39/137

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[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6565 - loss: 0.7621 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6529 - loss: 0.7685
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6543 - loss: 0.7684
[1m146/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6564 - loss: 0.7656
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6577 - loss: 0.7638 - val_accuracy: 0.6170 - val_loss: 0.8189
Epoch 40/137

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8161
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6668 - loss: 0.7615 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6696 - loss: 0.7522
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6688 - loss: 0.7519
[1m149/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6680 - loss: 0.7518
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6674 - loss: 0.7515 - val_accuracy: 0.6216 - val_loss: 0.8163

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 691ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 58.77 [%]
F1-score capturado en la ejecución 29: 59.77 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           260 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 60,932 (238.02 KB)
 Trainable params: 60,932 (238.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:41[0m 849ms/step
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 770us/step  
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 733us/step
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 706us/step
[1m278/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 726us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m71/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 722us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 62.78 [%]
Global F1 score (validation) = 63.26 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[4.1878593e-01 5.7820785e-01 4.3282684e-04 2.5733425e-03]
 [4.6803015e-01 5.2954417e-01 7.7924517e-04 1.6464697e-03]
 [4.6803015e-01 5.2954417e-01 7.7924517e-04 1.6464697e-03]
 ...
 [4.4845596e-02 2.0787427e-02 9.0910387e-01 2.5263153e-02]
 [2.3566036e-02 1.0074242e-02 9.5608503e-01 1.0274770e-02]
 [3.8700603e-02 1.7669644e-02 9.2324001e-01 2.0389643e-02]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 70.26 [%]
Global accuracy score (test) = 59.16 [%]
Global F1 score (train) = 70.38 [%]
Global F1 score (test) = 59.76 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.40      0.40       400
MODERATE-INTENSITY       0.47      0.52      0.49       400
         SEDENTARY       0.71      0.85      0.77       400
VIGOROUS-INTENSITY       0.91      0.61      0.73       345

          accuracy                           0.59      1545
         macro avg       0.62      0.59      0.60      1545
      weighted avg       0.61      0.59      0.59      1545


Accuracy capturado en la ejecución 30: 59.16 [%]
F1-score capturado en la ejecución 30: 59.76 [%]

=== RESUMEN FINAL ===
Accuracies: [57.54, 60.26, 59.81, 56.96, 59.22, 60.0, 58.77, 59.16, 59.61, 58.51, 58.51, 58.51, 57.73, 58.51, 58.32, 58.12, 59.74, 58.51, 58.25, 57.28, 59.81, 56.96, 60.06, 57.8, 61.1, 59.29, 58.9, 58.38, 58.77, 59.16]
F1-scores: [57.94, 60.24, 59.46, 57.57, 58.91, 59.84, 58.42, 59.19, 60.79, 58.68, 58.13, 59.18, 57.71, 58.84, 58.4, 57.96, 60.29, 58.68, 58.47, 57.92, 59.44, 56.95, 60.58, 58.16, 60.35, 59.14, 59.39, 58.4, 59.77, 59.76]
Accuracy mean: 58.7850 | std: 0.9852
F1 mean: 58.9520 | std: 0.9574

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