2025-11-05 10:12:46.793034: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:12:46.805041: 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 00:00:1762333966.819498 2747329 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762333966.823851 2747329 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762333966.834669 2747329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762333966.834689 2747329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762333966.834692 2747329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762333966.834694 2747329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:12:46.838018: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-05 10:12:49,729	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-05 10:12:50,433	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-05 10:12:50,508	INFO trial.py:182 -- Creating a new dirname dir_9a574_6fe0 because trial dirname 'dir_9a574' already exists.
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2025-11-05 10:12:50,532	INFO trial.py:182 -- Creating a new dirname dir_9a574_b3d5 because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,535	INFO trial.py:182 -- Creating a new dirname dir_9a574_257e because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,538	INFO trial.py:182 -- Creating a new dirname dir_9a574_f76d because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,541	INFO trial.py:182 -- Creating a new dirname dir_9a574_e7bc because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,545	INFO trial.py:182 -- Creating a new dirname dir_9a574_b0eb because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,548	INFO trial.py:182 -- Creating a new dirname dir_9a574_9c59 because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,552	INFO trial.py:182 -- Creating a new dirname dir_9a574_eb9f because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,556	INFO trial.py:182 -- Creating a new dirname dir_9a574_3747 because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,563	INFO trial.py:182 -- Creating a new dirname dir_9a574_d463 because trial dirname 'dir_9a574' already exists.
2025-11-05 10:12:50,569	INFO trial.py:182 -- Creating a new dirname dir_9a574_d12c because trial dirname 'dir_9a574' 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_PI/case_PI_ESANN_acc_superclasses_CPA_METs/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-05_10-12-49_041776_2747329/artifacts/2025-11-05_10-12-50/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-05 10:12:50. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    PENDING            2   rmsprop         tanh                                   64                128                  3          0.00010393         121 │
│ trial_9a574    PENDING            2   adam            tanh                                   32                 32                  5          0.000988608         96 │
│ trial_9a574    PENDING            4   adam            tanh                                   32                128                  3          0.000806034        114 │
│ trial_9a574    PENDING            3   adam            tanh                                   64                128                  3          0.00111323         129 │
│ trial_9a574    PENDING            4   adam            tanh                                   64                 64                  5          2.45513e-05         56 │
│ trial_9a574    PENDING            2   adam            relu                                   32                 32                  3          0.000549035        144 │
│ trial_9a574    PENDING            2   rmsprop         relu                                   32                 32                  5          0.000287988        131 │
│ trial_9a574    PENDING            2   rmsprop         relu                                   64                128                  3          1.86278e-05        122 │
│ trial_9a574    PENDING            3   adam            tanh                                   64                 64                  5          0.000232116        107 │
│ trial_9a574    PENDING            2   adam            relu                                   64                 64                  3          8.56913e-05         60 │
│ trial_9a574    PENDING            2   rmsprop         relu                                  128                 64                  3          0.00221256         108 │
│ trial_9a574    PENDING            2   rmsprop         relu                                  128                 32                  5          0.000224265         76 │
│ trial_9a574    PENDING            2   adam            tanh                                  128                128                  5          9.17574e-05        136 │
│ trial_9a574    PENDING            3   adam            relu                                   32                 32                  5          6.83635e-05         82 │
│ trial_9a574    PENDING            2   rmsprop         tanh                                  128                 32                  5          0.0007054           73 │
│ trial_9a574    PENDING            3   rmsprop         tanh                                  128                 32                  5          0.00391356         102 │
│ trial_9a574    PENDING            3   adam            tanh                                   64                 32                  3          0.00388748         144 │
│ trial_9a574    PENDING            3   adam            tanh                                  128                 64                  5          2.75265e-05         93 │
│ trial_9a574    PENDING            4   adam            tanh                                   64                 64                  3          0.000920926         56 │
│ trial_9a574    PENDING            4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           131 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00029 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            96 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00099 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           102 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00391 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
[36m(train_cnn_ray_tune pid=2748972)[0m 2025-11-05 10:12:53.530744: 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`.
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           122 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           108 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00221 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            56 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00092 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           114 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00081 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            56 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           107 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00023 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            73 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00071 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           129 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00111 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           136 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           144 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00389 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
[36m(train_cnn_ray_tune pid=2748972)[0m 2025-11-05 10:12:53.551656: 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=2748972)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=2748972)[0m E0000 00:00:1762333973.577538 2750100 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=2748972)[0m E0000 00:00:1762333973.585622 2750100 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=2748972)[0m W0000 00:00:1762333973.605023 2750100 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=2748972)[0m W0000 00:00:1762333973.605073 2750100 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=2748972)[0m W0000 00:00:1762333973.605076 2750100 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=2748972)[0m W0000 00:00:1762333973.605078 2750100 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=2748972)[0m 2025-11-05 10:12:53.611563: 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=2748972)[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=2748972)[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=2748972)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=2748972)[0m 2025-11-05 10:12:56.746315: 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=2748972)[0m 2025-11-05 10:12:56.746375: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=2748972)[0m 2025-11-05 10:12:56.746385: 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=2748972)[0m 2025-11-05 10:12:56.746391: 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=2748972)[0m 2025-11-05 10:12:56.746398: 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=2748972)[0m 2025-11-05 10:12:56.746401: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=2748972)[0m 2025-11-05 10:12:56.746685: 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=2748972)[0m 2025-11-05 10:12:56.746731: 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=2748972)[0m 2025-11-05 10:12:56.746736: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            58 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje              0.0001 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           121 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje              0.0001 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            93 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           144 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00055 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            82 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            60 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_9a574 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9a574 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            76 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00022 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748972)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=2748972)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=2748972)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=2748972)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=2748972)[0m │ conv1d (Conv1D)                 │ (None, 3, 32)          │        40,032 │
[36m(train_cnn_ray_tune pid=2748972)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2748972)[0m │ layer_normalization             │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2748972)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2748972)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2748972)[0m │ dropout (Dropout)               │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2748972)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2748972)[0m │ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         5,152 │
[36m(train_cnn_ray_tune pid=2748972)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2748972)[0m │ layer_normalization_1           │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2748972)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2748972)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2748972)[0m │ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2748972)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2748972)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=2748972)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=2748972)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2748972)[0m │ dropout_2 (Dropout)             │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=2748972)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2748972)[0m │ dense (Dense)                   │ (None, 4)              │           132 │
[36m(train_cnn_ray_tune pid=2748972)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=2748972)[0m  Total params: 45,444 (177.52 KB)
[36m(train_cnn_ray_tune pid=2748972)[0m  Trainable params: 45,444 (177.52 KB)
[36m(train_cnn_ray_tune pid=2748972)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=2748972)[0m Epoch 1/131
[36m(train_cnn_ray_tune pid=2748969)[0m  Total params: 407,812 (1.56 MB)
[36m(train_cnn_ray_tune pid=2748969)[0m  Trainable params: 407,812 (1.56 MB)
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10:50[0m 2s/step - accuracy: 0.2500 - loss: 2.5413
[1m  4/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 17ms/step - accuracy: 0.2910 - loss: 2.4812 
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m  8/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 16ms/step - accuracy: 0.2916 - loss: 2.4949
[1m 12/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 16ms/step - accuracy: 0.2928 - loss: 2.4751
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m 18/310[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.2953 - loss: 2.4468
[1m 23/310[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 13ms/step - accuracy: 0.2965 - loss: 2.4223
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m 27/310[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.2979 - loss: 2.4035
[1m 31/310[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.2984 - loss: 2.3952
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m 35/310[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.2987 - loss: 2.3866
[1m 40/310[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 13ms/step - accuracy: 0.2991 - loss: 2.3766
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m 46/310[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 13ms/step - accuracy: 0.3001 - loss: 2.3659
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m 50/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 13ms/step - accuracy: 0.3003 - loss: 2.3597
[1m 55/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 13ms/step - accuracy: 0.3008 - loss: 2.3518
[36m(train_cnn_ray_tune pid=2748970)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:53[0m 3s/step - accuracy: 0.1875 - loss: 2.4979
[36m(train_cnn_ray_tune pid=2748970)[0m 
[1m  5/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 15ms/step - accuracy: 0.2401 - loss: 2.4880 
[36m(train_cnn_ray_tune pid=2748967)[0m 
[1m 4/78[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.2476 - loss: 2.5410
[1m 7/78[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.2806 - loss: 2.3919
[36m(train_cnn_ray_tune pid=2748967)[0m 
[1m10/78[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.2959 - loss: 2.3125
[1m12/78[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.3048 - loss: 2.2717
[36m(train_cnn_ray_tune pid=2748956)[0m 
[1m10/78[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.2560 - loss: 2.7257
[36m(train_cnn_ray_tune pid=2748969)[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=2748969)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m │ global_average_pooling1d        │ (None, 128)            │             0 │[32m [repeated 104x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 194x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m │ layer_normalization             │ (None, 3, 128)         │           256 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 52x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m │ dropout (Dropout)               │ (None, 3, 128)         │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m │ dropout_4 (Dropout)             │ (None, 128)            │             0 │[32m [repeated 52x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m │ dense (Dense)                   │ (None, 4)              │           516 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748978)[0m  Total params: 30,564 (119.39 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=2748978)[0m  Trainable params: 30,564 (119.39 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748955)[0m Epoch 1/93[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748979)[0m 
[1m  3/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 29ms/step - accuracy: 0.2031 - loss: 3.0283 
[1m  5/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 29ms/step - accuracy: 0.1944 - loss: 3.0361
[36m(train_cnn_ray_tune pid=2748967)[0m 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 36ms/step - accuracy: 0.3679 - loss: 1.8250 - val_accuracy: 0.5400 - val_loss: 0.8972
[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m Epoch 3/102[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m Epoch 2/129[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m Epoch 7/73[32m [repeated 21x across cluster][0m
[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 10:13:20. 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_9a574    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00010393         121 │
│ trial_9a574    RUNNING            2   adam            tanh                                   32                 32                  5          0.000988608         96 │
│ trial_9a574    RUNNING            4   adam            tanh                                   32                128                  3          0.000806034        114 │
│ trial_9a574    RUNNING            3   adam            tanh                                   64                128                  3          0.00111323         129 │
│ trial_9a574    RUNNING            4   adam            tanh                                   64                 64                  5          2.45513e-05         56 │
│ trial_9a574    RUNNING            2   adam            relu                                   32                 32                  3          0.000549035        144 │
│ trial_9a574    RUNNING            2   rmsprop         relu                                   32                 32                  5          0.000287988        131 │
│ trial_9a574    RUNNING            2   rmsprop         relu                                   64                128                  3          1.86278e-05        122 │
│ trial_9a574    RUNNING            3   adam            tanh                                   64                 64                  5          0.000232116        107 │
│ trial_9a574    RUNNING            2   adam            relu                                   64                 64                  3          8.56913e-05         60 │
│ trial_9a574    RUNNING            2   rmsprop         relu                                  128                 64                  3          0.00221256         108 │
│ trial_9a574    RUNNING            2   rmsprop         relu                                  128                 32                  5          0.000224265         76 │
│ trial_9a574    RUNNING            2   adam            tanh                                  128                128                  5          9.17574e-05        136 │
│ trial_9a574    RUNNING            3   adam            relu                                   32                 32                  5          6.83635e-05         82 │
│ trial_9a574    RUNNING            2   rmsprop         tanh                                  128                 32                  5          0.0007054           73 │
│ trial_9a574    RUNNING            3   rmsprop         tanh                                  128                 32                  5          0.00391356         102 │
│ trial_9a574    RUNNING            3   adam            tanh                                   64                 32                  3          0.00388748         144 │
│ trial_9a574    RUNNING            3   adam            tanh                                  128                 64                  5          2.75265e-05         93 │
│ trial_9a574    RUNNING            4   adam            tanh                                   64                 64                  3          0.000920926         56 │
│ trial_9a574    RUNNING            4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m Epoch 8/108[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m Epoch 9/102[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748955)[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=2748955)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748975)[0m 2025-11-05 10:12:54.200070: 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=2748975)[0m 2025-11-05 10:12:54.225712: 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=2748975)[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=2748975)[0m E0000 00:00:1762333974.250466 2750240 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=2748975)[0m E0000 00:00:1762333974.257426 2750240 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=2748975)[0m W0000 00:00:1762333974.275411 2750240 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=2748975)[0m 2025-11-05 10:12:54.280931: 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=2748975)[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=2748968)[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=2748968)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748955)[0m 2025-11-05 10:12:57.563673: 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=2748955)[0m 2025-11-05 10:12:57.563732: 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=2748955)[0m 2025-11-05 10:12:57.563740: 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=2748955)[0m 2025-11-05 10:12:57.563744: 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=2748955)[0m 2025-11-05 10:12:57.563749: 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=2748955)[0m 2025-11-05 10:12:57.563753: 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=2748955)[0m 2025-11-05 10:12:57.563998: 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=2748955)[0m 2025-11-05 10:12:57.564045: 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=2748955)[0m 2025-11-05 10:12:57.564049: 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
Trial trial_9a574 finished iteration 1 at 2025-11-05 10:13:38. Total running time: 48s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             44.8199 │
│ time_total_s                 44.8199 │
│ training_iteration                 1 │
│ val_accuracy                 0.45787 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:13:38. Total running time: 48s
[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m Epoch 16/73[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 119ms/step - accuracy: 0.4531 - loss: 1.6845
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:43[0m 2s/step - accuracy: 0.5000 - loss: 1.0126[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
[1m155/155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 49ms/step - accuracy: 0.2861 - loss: 2.4300 - val_accuracy: 0.4537 - val_loss: 1.4807[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
[1m  3/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 46ms/step - accuracy: 0.3099 - loss: 2.5484  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2748975)[0m 
[1m60/78[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 27ms/step - accuracy: 0.4946 - loss: 1.1282
[1m62/78[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 27ms/step - accuracy: 0.4948 - loss: 1.1278
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
[1m 3/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 39ms/step - accuracy: 0.6905 - loss: 0.7186 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2748975)[0m Epoch 18/76[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 53ms/step - accuracy: 0.4523 - loss: 1.6514 - val_accuracy: 0.5945 - val_loss: 0.9352[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2748979)[0m 
[1m  3/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2812 - loss: 2.1025  
[1m  5/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2837 - loss: 2.1299[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2748968)[0m 
[1m  3/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 35ms/step - accuracy: 0.5069 - loss: 1.4925 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
[1m175/310[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.5223 - loss: 1.0713
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-05 10:13:50. Total running time: 1min 0s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                   32                 32                  5          0.000988608         96                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                128                  3          0.00111323         129                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  5          2.45513e-05         56                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   32                 32                  3          0.000549035        144                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   32                 32                  5          0.000287988        131                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   64                 64                  3          8.56913e-05         60                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                  128                 64                  3          0.00221256         108                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                  128                 32                  5          0.000224265         76                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              2   rmsprop         tanh                                  128                 32                  5          0.0007054           73                                              │
│ trial_9a574    RUNNING              3   rmsprop         tanh                                  128                 32                  5          0.00391356         102                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 32                  3          0.00388748         144                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  3          0.000920926         56                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m Epoch 17/102[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
[1m33/43[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[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=2748971)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748971)[0m 
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[36m(train_cnn_ray_tune pid=2748971)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:13:56. Total running time: 1min 5s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             62.4294 │
│ time_total_s                 62.4294 │
│ training_iteration                 1 │
│ val_accuracy                 0.45295 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:13:56. Total running time: 1min 5s
[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m Epoch 12/60[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m Epoch 8/129[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748967)[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=2748967)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748967)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:14:08. Total running time: 1min 18s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             75.3104 │
│ time_total_s                 75.3104 │
│ training_iteration                 1 │
│ val_accuracy                 0.71805 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:14:08. Total running time: 1min 18s
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[1m  5/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.4848 - loss: 1.4957[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2748975)[0m Epoch 28/76[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[1m  3/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 59ms/step - accuracy: 0.5321 - loss: 1.0272
[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
[1m 9/78[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.5493 - loss: 0.9549
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m Epoch 11/56[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-05 10:14:20. Total running time: 1min 30s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                   32                 32                  5          0.000988608         96                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                128                  3          0.00111323         129                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   32                 32                  3          0.000549035        144                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   32                 32                  5          0.000287988        131                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   64                 64                  3          8.56913e-05         60                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                  128                 32                  5          0.000224265         76                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              2   rmsprop         tanh                                  128                 32                  5          0.0007054           73                                              │
│ trial_9a574    RUNNING              3   rmsprop         tanh                                  128                 32                  5          0.00391356         102                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 32                  3          0.00388748         144                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  3          0.000920926         56                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748956)[0m 
[1m21/78[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6166 - loss: 0.9119
[1m24/78[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6160 - loss: 0.9100[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=2748982)[0m 
[1m 3/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 35ms/step - accuracy: 0.5829 - loss: 0.9443 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
[1m  4/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.2982 - loss: 1.8800  
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[36m(train_cnn_ray_tune pid=2748977)[0m Epoch 15/122[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m Epoch 16/122[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m Epoch 38/73[32m [repeated 21x across cluster][0m
[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 250ms/step
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
[1m34/43[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=2748982)[0m 
[1m41/43[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[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=2748982)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m Epoch 13/96[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748982)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:14:38. Total running time: 1min 48s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             105.467 │
│ time_total_s                 105.467 │
│ training_iteration                 1 │
│ val_accuracy                 0.63553 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:14:38. Total running time: 1min 48s
[36m(train_cnn_ray_tune pid=2748982)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m Epoch 11/58[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m Epoch 16/56[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-05 10:14:50. Total running time: 2min 0s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                   32                 32                  5          0.000988608         96                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                128                  3          0.00111323         129                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   32                 32                  3          0.000549035        144                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   32                 32                  5          0.000287988        131                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   64                 64                  3          8.56913e-05         60                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                  128                 32                  5          0.000224265         76                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              2   rmsprop         tanh                                  128                 32                  5          0.0007054           73                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 32                  3          0.00388748         144                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  3          0.000920926         56                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m Epoch 47/76[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[0m 
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[36m(train_cnn_ray_tune pid=2748978)[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=2748978)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748975)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:14:54. Total running time: 2min 3s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             120.837 │
│ time_total_s                 120.837 │
│ training_iteration                 1 │
│ val_accuracy                 0.61552 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:14:54. Total running time: 2min 3s
[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m Epoch 16/129[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m Epoch 13/82[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m Epoch 19/131[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m36/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 38ms/step - accuracy: 0.5409 - loss: 1.1377[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m Epoch 34/136[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
[1m 56/155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.5358 - loss: 1.0451
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m Epoch 32/60[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-11-05 10:15:21. Total running time: 2min 30s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                   32                 32                  5          0.000988608         96                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                128                  3          0.00111323         129                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   32                 32                  3          0.000549035        144                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   32                 32                  5          0.000287988        131                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   64                 64                  3          8.56913e-05         60                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                  128                 32                  5          0.000224265         76                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              2   rmsprop         tanh                                  128                 32                  5          0.0007054           73                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  3          0.000920926         56                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m Epoch 26/107[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748956)[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=2748956)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748979)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:15:27. Total running time: 2min 36s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             153.467 │
│ time_total_s                 153.467 │
│ training_iteration                 1 │
│ val_accuracy                 0.66362 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:15:27. Total running time: 2min 36s
[36m(train_cnn_ray_tune pid=2748975)[0m Epoch 65/76[32m [repeated 15x across cluster][0m
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[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=2748975)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m Epoch 17/82[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748975)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:15:34. Total running time: 2min 44s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             160.872 │
│ time_total_s                 160.872 │
│ training_iteration                 1 │
│ val_accuracy                 0.69101 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:15:34. Total running time: 2min 44s
[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m Epoch 32/122[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m Epoch 24/96[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m54/78[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 29ms/step - accuracy: 0.5538 - loss: 1.0718[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748980)[0m 
[1m  4/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6061 - loss: 0.9800 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m Epoch 20/58[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 35ms/step - accuracy: 0.5563 - loss: 1.0654 - val_accuracy: 0.6331 - val_loss: 0.8313
[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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Trial status: 13 RUNNING | 7 TERMINATED
Current time: 2025-11-05 10:15:51. Total running time: 3min 0s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                   32                 32                  5          0.000988608         96                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                128                  3          0.00111323         129                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   32                 32                  3          0.000549035        144                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   32                 32                  5          0.000287988        131                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   64                 64                  3          8.56913e-05         60                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  3          0.000920926         56                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 32                  5          0.000224265         76        1           160.872          0.691011 │
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.0007054           73        1           153.467          0.663624 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
[1m 79/310[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.6119 - loss: 0.8980
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m Epoch 36/122[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m Epoch 28/131[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m Epoch 51/136[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m Epoch 53/136[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m Epoch 24/58[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m Epoch 33/56[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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Trial status: 13 RUNNING | 7 TERMINATED
Current time: 2025-11-05 10:16:21. Total running time: 3min 30s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                   32                 32                  5          0.000988608         96                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                128                  3          0.00111323         129                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   32                 32                  3          0.000549035        144                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   32                 32                  5          0.000287988        131                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   64                 64                  3          8.56913e-05         60                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  3          0.000920926         56                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 32                  5          0.000224265         76        1           160.872          0.691011 │
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.0007054           73        1           153.467          0.663624 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m Epoch 32/144[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m Epoch 60/136[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m Epoch 43/107[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m Epoch 57/60[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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Trial status: 13 RUNNING | 7 TERMINATED
Current time: 2025-11-05 10:16:51. Total running time: 4min 0s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                   32                 32                  5          0.000988608         96                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                128                  3          0.00111323         129                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   32                 32                  3          0.000549035        144                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   32                 32                  5          0.000287988        131                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            relu                                   64                 64                  3          8.56913e-05         60                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  3          0.000920926         56                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 32                  5          0.000224265         76        1           160.872          0.691011 │
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.0007054           73        1           153.467          0.663624 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[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=2748968)[0m   _log_deprecation_warning(
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
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[36m(train_cnn_ray_tune pid=2748968)[0m 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step
[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 33ms/step - accuracy: 0.5951 - loss: 0.9681 - val_accuracy: 0.6440 - val_loss: 0.8038

Trial trial_9a574 finished iteration 1 at 2025-11-05 10:16:54. Total running time: 4min 3s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             240.517 │
│ time_total_s                 240.517 │
│ training_iteration                 1 │
│ val_accuracy                 0.67451 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:16:54. Total running time: 4min 3s
[36m(train_cnn_ray_tune pid=2748976)[0m Epoch 69/136[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m 7/78[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 28ms/step - accuracy: 0.5898 - loss: 1.0049
[1m 9/78[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 28ms/step - accuracy: 0.5913 - loss: 0.9965
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[1m 25/310[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 12ms/step - accuracy: 0.6778 - loss: 0.7586
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 70ms/step - accuracy: 0.6875 - loss: 0.8319
[1m  6/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 12ms/step - accuracy: 0.6550 - loss: 0.8580 [32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
[1m278/310[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 16ms/step - accuracy: 0.4584 - loss: 1.1802[32m [repeated 137x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 79ms/step - accuracy: 0.6016 - loss: 0.9403[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m23/78[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 26ms/step - accuracy: 0.6163 - loss: 0.9350
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
[1m11/43[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step  
[1m21/43[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=2748970)[0m 
[1m30/43[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 6ms/step
[1m39/43[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m Epoch 71/136[32m [repeated 13x across cluster][0m
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[36m(train_cnn_ray_tune pid=2748970)[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=2748970)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748970)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:17:00. Total running time: 4min 9s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             246.664 │
│ time_total_s                 246.664 │
│ training_iteration                 1 │
│ val_accuracy                 0.68294 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:17:00. Total running time: 4min 9s
[36m(train_cnn_ray_tune pid=2748970)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:17:01. Total running time: 4min 11s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s               247.8 │
│ time_total_s                   247.8 │
│ training_iteration                 1 │
│ val_accuracy                 0.70435 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:17:01. Total running time: 4min 11s
[36m(train_cnn_ray_tune pid=2748973)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
[1m12/43[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step  
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748974)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m Epoch 21/114[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748973)[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=2748973)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=2748973)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:17:11. Total running time: 4min 21s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             257.878 │
│ time_total_s                 257.878 │
│ training_iteration                 1 │
│ val_accuracy                 0.69944 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:17:11. Total running time: 4min 21s
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m Epoch 78/136[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m Epoch 34/82[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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Trial status: 9 RUNNING | 11 TERMINATED
Current time: 2025-11-05 10:17:21. Total running time: 4min 30s
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_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   32                 32                  5          0.000287988        131                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   64                 64                  3          0.000920926         56                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           2   adam            tanh                                   32                 32                  5          0.000988608         96        1           246.664          0.682935 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                128                  3          0.00111323         129        1           257.878          0.699438 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   adam            relu                                   32                 32                  3          0.000549035        144        1           247.8            0.704354 │
│ trial_9a574    TERMINATED           2   adam            relu                                   64                 64                  3          8.56913e-05         60        1           240.517          0.674508 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 32                  5          0.000224265         76        1           160.872          0.691011 │
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.0007054           73        1           153.467          0.663624 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m104/310[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6655 - loss: 0.7879
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m145/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6638 - loss: 0.7917
[1m150/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6636 - loss: 0.7918
[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m155/310[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6634 - loss: 0.7918
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
[1m165/310[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.6630 - loss: 0.7918
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m Epoch 60/121[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[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=2748981)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748981)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:17:33. Total running time: 4min 43s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              280.13 │
│ time_total_s                  280.13 │
│ training_iteration                 1 │
│ val_accuracy                 0.62956 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:17:33. Total running time: 4min 43s
[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m Epoch 49/131[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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[36m(train_cnn_ray_tune pid=2748972)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:17:39. Total running time: 4min 49s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             286.317 │
│ time_total_s                 286.317 │
│ training_iteration                 1 │
│ val_accuracy                 0.69487 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:17:39. Total running time: 4min 49s
[36m(train_cnn_ray_tune pid=2748972)[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=2748972)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m Epoch 64/121[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m Epoch 40/82[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 19ms/step - accuracy: 0.6415 - loss: 0.8568 - val_accuracy: 0.6492 - val_loss: 0.7749
[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 65ms/step - accuracy: 0.6328 - loss: 0.9033
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m Epoch 43/58[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-11-05 10:17:51. Total running time: 5min 0s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              4   adam            tanh                                   32                128                  3          0.000806034        114                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            tanh                                   64                 64                  5          0.000232116        107                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           2   adam            tanh                                   32                 32                  5          0.000988608         96        1           246.664          0.682935 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                128                  3          0.00111323         129        1           257.878          0.699438 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   adam            relu                                   32                 32                  3          0.000549035        144        1           247.8            0.704354 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                   32                 32                  5          0.000287988        131        1           286.317          0.694874 │
│ trial_9a574    TERMINATED           2   adam            relu                                   64                 64                  3          8.56913e-05         60        1           240.517          0.674508 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 32                  5          0.000224265         76        1           160.872          0.691011 │
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.0007054           73        1           153.467          0.663624 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  3          0.000920926         56        1           280.13           0.629565 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m Epoch 29/114[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
[1m 75/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.5132 - loss: 1.0681 
[1m 81/310[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.5143 - loss: 1.0673
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m23/78[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step - accuracy: 0.6417 - loss: 0.8415
[36m(train_cnn_ray_tune pid=2748969)[0m 
[1m  1/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 71ms/step - accuracy: 0.6875 - loss: 0.6816
[1m  4/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6423 - loss: 0.7938 [32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2748980)[0m 
[1m 99/155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.6450 - loss: 0.7990
[1m103/155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.6448 - loss: 0.7996[32m [repeated 182x across cluster][0m
[36m(train_cnn_ray_tune pid=2748954)[0m 
[1m142/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 15ms/step - accuracy: 0.6918 - loss: 0.7107
[1m145/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 15ms/step - accuracy: 0.6918 - loss: 0.7108
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[36m(train_cnn_ray_tune pid=2748980)[0m 
[1m155/155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.6419 - loss: 0.8050[32m [repeated 57x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[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=2748954)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748954)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:18:02. Total running time: 5min 11s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             308.646 │
│ time_total_s                 308.646 │
│ training_iteration                 1 │
│ val_accuracy                 0.69909 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:18:02. Total running time: 5min 11s
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m Epoch 74/107[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
[1m  1/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 35ms/step - accuracy: 0.6250 - loss: 0.7108
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[1m 69/155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 16ms/step - accuracy: 0.6130 - loss: 0.9280
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[36m(train_cnn_ray_tune pid=2748977)[0m 
[1m  7/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6242 - loss: 0.8769 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 10ms/step - accuracy: 0.5126 - loss: 1.0677 - val_accuracy: 0.6159 - val_loss: 0.8282[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
[1m  1/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 62ms/step - accuracy: 0.6250 - loss: 0.8662[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m Epoch 78/121[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[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=2748980)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748980)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:18:19. Total running time: 5min 29s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             326.366 │
│ time_total_s                 326.366 │
│ training_iteration                 1 │
│ val_accuracy                 0.66292 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:18:19. Total running time: 5min 29s
[36m(train_cnn_ray_tune pid=2748980)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m Epoch 119/136[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
[1m  9/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.5924 - loss: 0.9774  
[1m 17/310[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.5875 - loss: 0.9691

Trial status: 5 RUNNING | 15 TERMINATED
Current time: 2025-11-05 10:18:21. Total running time: 5min 30s
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_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              2   adam            tanh                                  128                128                  5          9.17574e-05        136                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    RUNNING              4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58                                              │
│ trial_9a574    TERMINATED           2   adam            tanh                                   32                 32                  5          0.000988608         96        1           246.664          0.682935 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   32                128                  3          0.000806034        114        1           308.645          0.699087 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                128                  3          0.00111323         129        1           257.878          0.699438 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   adam            relu                                   32                 32                  3          0.000549035        144        1           247.8            0.704354 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                   32                 32                  5          0.000287988        131        1           286.317          0.694874 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 64                  5          0.000232116        107        1           326.366          0.662921 │
│ trial_9a574    TERMINATED           2   adam            relu                                   64                 64                  3          8.56913e-05         60        1           240.517          0.674508 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 32                  5          0.000224265         76        1           160.872          0.691011 │
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.0007054           73        1           153.467          0.663624 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  3          0.000920926         56        1           280.13           0.629565 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m Epoch 55/58[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m Epoch 57/58[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
[1m  1/155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 54ms/step - accuracy: 0.6875 - loss: 0.9056
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
[1m155/155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 10ms/step - accuracy: 0.5960 - loss: 0.9662 - val_accuracy: 0.6738 - val_loss: 0.6884[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2748969)[0m 
[1m126/155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step - accuracy: 0.6167 - loss: 0.8833[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 50ms/step - accuracy: 0.6016 - loss: 0.9029[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m Epoch 93/121[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[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=2748969)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748969)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:18:36. Total running time: 5min 45s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             342.497 │
│ time_total_s                 342.497 │
│ training_iteration                 1 │
│ val_accuracy                 0.64326 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:18:36. Total running time: 5min 45s
[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[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=2748976)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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[36m(train_cnn_ray_tune pid=2748969)[0m 
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[36m(train_cnn_ray_tune pid=2748976)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:18:38. Total running time: 5min 48s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             345.322 │
│ time_total_s                 345.322 │
│ training_iteration                 1 │
│ val_accuracy                 0.66362 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:18:38. Total running time: 5min 48s
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[36m(train_cnn_ray_tune pid=2748953)[0m Epoch 98/121[32m [repeated 16x across cluster][0m
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m Epoch 110/122[32m [repeated 17x across cluster][0m
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m Epoch 110/121[32m [repeated 16x across cluster][0m

Trial status: 3 RUNNING | 17 TERMINATED
Current time: 2025-11-05 10:18:51. Total running time: 6min 0s
Logical resource usage: 3.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00010393         121                                              │
│ trial_9a574    RUNNING              2   rmsprop         relu                                   64                128                  3          1.86278e-05        122                                              │
│ trial_9a574    RUNNING              3   adam            relu                                   32                 32                  5          6.83635e-05         82                                              │
│ trial_9a574    TERMINATED           2   adam            tanh                                   32                 32                  5          0.000988608         96        1           246.664          0.682935 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   32                128                  3          0.000806034        114        1           308.645          0.699087 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                128                  3          0.00111323         129        1           257.878          0.699438 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   adam            relu                                   32                 32                  3          0.000549035        144        1           247.8            0.704354 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                   32                 32                  5          0.000287988        131        1           286.317          0.694874 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 64                  5          0.000232116        107        1           326.366          0.662921 │
│ trial_9a574    TERMINATED           2   adam            relu                                   64                 64                  3          8.56913e-05         60        1           240.517          0.674508 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 32                  5          0.000224265         76        1           160.872          0.691011 │
│ trial_9a574    TERMINATED           2   adam            tanh                                  128                128                  5          9.17574e-05        136        1           345.322          0.663624 │
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.0007054           73        1           153.467          0.663624 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  3          0.000920926         56        1           280.13           0.629565 │
│ trial_9a574    TERMINATED           4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58        1           342.497          0.643258 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2748953)[0m 
[1m148/155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.6889 - loss: 0.7142[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[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=2748977)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748953)[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=2748953)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748977)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m Epoch 116/121[32m [repeated 16x across cluster][0m
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:18:56. Total running time: 6min 6s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             363.151 │
│ time_total_s                 363.151 │
│ training_iteration                 1 │
│ val_accuracy                 0.68223 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:18:56. Total running time: 6min 6s
[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:19:00. Total running time: 6min 9s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             366.534 │
│ time_total_s                 366.534 │
│ training_iteration                 1 │
│ val_accuracy                 0.67872 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:19:00. Total running time: 6min 9s
2025-11-05 10:19:07,175	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_ESANN_acc_superclasses_CPA_METs/ESANN_hyperparameters_tuning' in 0.0075s.
[36m(train_cnn_ray_tune pid=2748979)[0m Epoch 76/82[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748953)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m 
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[36m(train_cnn_ray_tune pid=2748979)[0m Epoch 82/82[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2748979)[0m 
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Trial trial_9a574 finished iteration 1 at 2025-11-05 10:19:07. Total running time: 6min 16s
╭──────────────────────────────────────╮
│ Trial trial_9a574 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             373.486 │
│ time_total_s                 373.486 │
│ training_iteration                 1 │
│ val_accuracy                 0.63764 │
╰──────────────────────────────────────╯

Trial trial_9a574 completed after 1 iterations at 2025-11-05 10:19:07. Total running time: 6min 16s

Trial status: 20 TERMINATED
Current time: 2025-11-05 10:19:07. Total running time: 6min 16s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
[36m(train_cnn_ray_tune pid=2748979)[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=2748979)[0m   _log_deprecation_warning(
/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 00:00:1762334347.310468 2747329 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.00010393         121        1           366.534          0.678722 │
│ trial_9a574    TERMINATED           2   adam            tanh                                   32                 32                  5          0.000988608         96        1           246.664          0.682935 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   32                128                  3          0.000806034        114        1           308.645          0.699087 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                128                  3          0.00111323         129        1           257.878          0.699438 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  5          2.45513e-05         56        1            62.4294         0.452949 │
│ trial_9a574    TERMINATED           2   adam            relu                                   32                 32                  3          0.000549035        144        1           247.8            0.704354 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                   32                 32                  5          0.000287988        131        1           286.317          0.694874 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                   64                128                  3          1.86278e-05        122        1           363.151          0.682233 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 64                  5          0.000232116        107        1           326.366          0.662921 │
│ trial_9a574    TERMINATED           2   adam            relu                                   64                 64                  3          8.56913e-05         60        1           240.517          0.674508 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 64                  3          0.00221256         108        1            75.3104         0.718048 │
│ trial_9a574    TERMINATED           2   rmsprop         relu                                  128                 32                  5          0.000224265         76        1           160.872          0.691011 │
│ trial_9a574    TERMINATED           2   adam            tanh                                  128                128                  5          9.17574e-05        136        1           345.322          0.663624 │
│ trial_9a574    TERMINATED           3   adam            relu                                   32                 32                  5          6.83635e-05         82        1           373.486          0.63764  │
│ trial_9a574    TERMINATED           2   rmsprop         tanh                                  128                 32                  5          0.0007054           73        1           153.467          0.663624 │
│ trial_9a574    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.00391356         102        1           105.467          0.635534 │
│ trial_9a574    TERMINATED           3   adam            tanh                                   64                 32                  3          0.00388748         144        1           120.837          0.61552  │
│ trial_9a574    TERMINATED           3   adam            tanh                                  128                 64                  5          2.75265e-05         93        1            44.8199         0.457865 │
│ trial_9a574    TERMINATED           4   adam            tanh                                   64                 64                  3          0.000920926         56        1           280.13           0.629565 │
│ trial_9a574    TERMINATED           4   rmsprop         tanh                                   64                128                  5          9.60262e-05         58        1           342.497          0.643258 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

[36m(train_cnn_ray_tune pid=2748979)[0m 
[1m45/89[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'rmsprop', 'funcion_activacion': 'relu', 'tamanho_minilote': 128, 'numero_filtros': 64, 'tamanho_filtro': 3, 'tasa_aprendizaje': 0.0022125630046603085, 'epochs': 108}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334348.857013 2855779 service.cc:152] XLA service 0x7e56cc009a40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334348.857059 2855779 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:19:08.893365: 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 00:00:1762334349.017503 2855779 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334351.445903 2855779 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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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 2.1185 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3570 - loss: 1.9202
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Epoch 2/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4660 - loss: 1.2238 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4779 - loss: 1.1876 - val_accuracy: 0.6362 - val_loss: 0.8123
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1605
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5528 - loss: 1.0370 
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5859 - loss: 0.8366
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5902 - loss: 0.9213 
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0679
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5986 - loss: 0.8944 
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5781 - loss: 0.8139
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6353 - loss: 0.8330 
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Epoch 7/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6426 - loss: 0.8076 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6250 - loss: 0.8047
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6424 - loss: 0.7945 
[1m75/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6528 - loss: 0.7787
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6534 - loss: 0.7778 - val_accuracy: 0.7047 - val_loss: 0.6359
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.7188 - loss: 0.6469
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6732 - loss: 0.7206 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6673 - loss: 0.7341 - val_accuracy: 0.7079 - val_loss: 0.6342
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7336
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6714 - loss: 0.7367 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6758 - loss: 0.7346 - val_accuracy: 0.7142 - val_loss: 0.6290
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.6406 - loss: 0.8528
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6796 - loss: 0.7367 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6820 - loss: 0.7293 - val_accuracy: 0.7079 - val_loss: 0.6293
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7031 - loss: 0.6309
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6772 - loss: 0.7116 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6790 - loss: 0.7133 - val_accuracy: 0.6952 - val_loss: 0.6625
Epoch 13/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7006 - loss: 0.7116 
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Epoch 14/108

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Epoch 15/108

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Saved model to disk.
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=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m71/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 719us/step
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Global accuracy score (validation) = 70.86 [%]
Global F1 score (validation) = 71.77 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.4889109  0.49905047 0.00499926 0.00703937]
 [0.5771689  0.40388554 0.01156915 0.00737645]
 [0.57756823 0.40240845 0.01242747 0.00759581]
 ...
 [0.00217718 0.00416217 0.00322613 0.9904345 ]
 [0.00185561 0.0037643  0.00212532 0.9922548 ]
 [0.02595882 0.01556831 0.92987657 0.02859625]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.92 [%]
Global accuracy score (test) = 70.42 [%]
Global F1 score (train) = 75.37 [%]
Global F1 score (test) = 71.57 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.52      0.47      0.49       350
MODERATE-INTENSITY       0.49      0.59      0.53       350
         SEDENTARY       0.96      0.96      0.96       350
VIGOROUS-INTENSITY       0.96      0.80      0.87       299

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

2025-11-05 10:19:30.423559: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:19:30.434642: 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 00:00:1762334370.448074 2857984 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334370.452278 2857984 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334370.462196 2857984 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334370.462221 2857984 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334370.462222 2857984 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334370.462223 2857984 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:19:30.465327: 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 00:00:1762334372.702841 2857984 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334374.067217 2858099 service.cc:152] XLA service 0x74e75c01c400 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334374.067244 2858099 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:19:34.101742: 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 00:00:1762334374.223021 2858099 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334376.632582 2858099 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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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3349 - loss: 2.1681 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 30ms/step - accuracy: 0.3608 - loss: 1.9622
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 52ms/step - accuracy: 0.3614 - loss: 1.9584 - val_accuracy: 0.5804 - val_loss: 0.8857
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.4297 - loss: 1.2754
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.2378 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4710 - loss: 1.2098 - val_accuracy: 0.6236 - val_loss: 0.8119
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1142
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0757 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5282 - loss: 1.0570 - val_accuracy: 0.6615 - val_loss: 0.7480
Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5547 - loss: 1.0226
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5714 - loss: 0.9752 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5786 - loss: 0.9614 - val_accuracy: 0.6738 - val_loss: 0.7210
Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0055
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5953 - loss: 0.9174 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.6053 - loss: 0.8997
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6055 - loss: 0.8993 - val_accuracy: 0.6752 - val_loss: 0.6850
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6797 - loss: 0.8700
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6332 - loss: 0.8397 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6344 - loss: 0.8339 - val_accuracy: 0.6850 - val_loss: 0.6767
Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6328 - loss: 0.8124
[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6439 - loss: 0.8013 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6463 - loss: 0.7994 - val_accuracy: 0.6994 - val_loss: 0.6441
Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.6094 - loss: 0.8243
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6442 - loss: 0.7960 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6484 - loss: 0.7893 - val_accuracy: 0.6893 - val_loss: 0.6497
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6328 - loss: 0.7379
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6553 - loss: 0.7558 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6600 - loss: 0.7550 - val_accuracy: 0.6893 - val_loss: 0.6402
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 0.7361
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6675 - loss: 0.7450 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6673 - loss: 0.7456 - val_accuracy: 0.7159 - val_loss: 0.6301
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7109 - loss: 0.6771
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6925 - loss: 0.7082 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6846 - loss: 0.7214 - val_accuracy: 0.6956 - val_loss: 0.6444
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7344 - loss: 0.5964
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6867 - loss: 0.7339 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6843 - loss: 0.7333 - val_accuracy: 0.6984 - val_loss: 0.6382
Epoch 13/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6884 - loss: 0.7155 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6847 - loss: 0.7211 - val_accuracy: 0.7100 - val_loss: 0.6403
Epoch 14/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6818 - loss: 0.7160 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6830 - loss: 0.7125 - val_accuracy: 0.7054 - val_loss: 0.6335
Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7578 - loss: 0.7401
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6867 - loss: 0.7121 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6865 - loss: 0.7084 - val_accuracy: 0.7114 - val_loss: 0.6222
Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7422 - loss: 0.6342
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7073 - loss: 0.6750 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.7047 - loss: 0.6796
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7046 - loss: 0.6797 - val_accuracy: 0.6998 - val_loss: 0.6322
Epoch 17/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7188 - loss: 0.6900
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6973 - loss: 0.7029 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6988 - loss: 0.6954 - val_accuracy: 0.7068 - val_loss: 0.6208
Epoch 18/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6875 - loss: 0.6268
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6980 - loss: 0.6745 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7002 - loss: 0.6765 - val_accuracy: 0.6984 - val_loss: 0.6479
Epoch 19/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7031 - loss: 0.6803
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6958 - loss: 0.6941 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6983 - loss: 0.6910 - val_accuracy: 0.6980 - val_loss: 0.6537
Epoch 20/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6797 - loss: 0.7439
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6977 - loss: 0.6875 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7009 - loss: 0.6857 - val_accuracy: 0.7159 - val_loss: 0.6277
Epoch 21/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6797 - loss: 0.6558
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7097 - loss: 0.6550 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7074 - loss: 0.6660 - val_accuracy: 0.7177 - val_loss: 0.6212
Epoch 22/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7500 - loss: 0.6801
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7044 - loss: 0.6684 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.7056 - loss: 0.6639
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7056 - loss: 0.6639 - val_accuracy: 0.7163 - val_loss: 0.6210

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 371ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 1: 70.42 [%]
F1-score capturado en la ejecución 1: 71.57 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:36[0m 896ms/step
[1m 61/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 849us/step  
[1m134/310[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 763us/step
[1m213/310[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 717us/step
[1m284/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 714us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m72/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 705us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 823us/step
Global accuracy score (validation) = 71.49 [%]
Global F1 score (validation) = 70.98 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[3.5242829e-01 6.3946903e-01 3.3041512e-04 7.7722762e-03]
 [6.3272220e-01 3.6032912e-01 3.3480371e-03 3.6006651e-03]
 [5.3670299e-01 4.5650598e-01 2.0491860e-03 4.7418373e-03]
 ...
 [1.2538368e-03 2.9807391e-03 7.4865337e-04 9.9501681e-01]
 [6.8935705e-04 1.9501732e-03 3.7074974e-04 9.9698961e-01]
 [5.2016832e-02 3.8510609e-02 8.6014277e-01 4.9329810e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.43 [%]
Global accuracy score (test) = 71.02 [%]
Global F1 score (train) = 73.3 [%]
Global F1 score (test) = 70.56 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.50      0.80      0.61       350
MODERATE-INTENSITY       0.50      0.26      0.34       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.96      0.85      0.90       299

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

2025-11-05 10:19:54.592067: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:19:54.603299: 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 00:00:1762334394.616669 2860913 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334394.620768 2860913 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334394.630551 2860913 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334394.630568 2860913 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334394.630570 2860913 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334394.630571 2860913 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:19:54.633670: 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 00:00:1762334396.854364 2860913 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334398.241797 2861028 service.cc:152] XLA service 0x7a0480003540 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334398.241825 2861028 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:19:58.275712: 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 00:00:1762334398.393360 2861028 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334400.780659 2861028 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:13[0m 3s/step - accuracy: 0.2109 - loss: 2.8315
[1m30/78[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3328 - loss: 2.2365 
[1m70/78[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.9919
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 30ms/step - accuracy: 0.3619 - loss: 1.9576
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Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4922 - loss: 1.2213
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1955 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1720 - val_accuracy: 0.6225 - val_loss: 0.8066
Epoch 3/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0454 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5386 - loss: 1.0268 - val_accuracy: 0.6636 - val_loss: 0.7405
Epoch 4/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5867 - loss: 0.9427 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5920 - loss: 0.9316 - val_accuracy: 0.6633 - val_loss: 0.7233
Epoch 5/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6278 - loss: 0.8540 
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Epoch 6/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6340 - loss: 0.8303 
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Epoch 7/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6476 - loss: 0.7942 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9908
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6560 - loss: 0.7826 
[1m74/78[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.6619 - loss: 0.7728
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6623 - loss: 0.7723 - val_accuracy: 0.7029 - val_loss: 0.6410
Epoch 9/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6662 - loss: 0.7270 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6651 - loss: 0.7389 - val_accuracy: 0.6896 - val_loss: 0.6472
Epoch 10/108

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[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6721 - loss: 0.7571 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6740 - loss: 0.7501 - val_accuracy: 0.6840 - val_loss: 0.6594
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6797 - loss: 0.6716
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6826 - loss: 0.7304 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6801 - loss: 0.7270 - val_accuracy: 0.7037 - val_loss: 0.6453
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6094 - loss: 0.8033
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6800 - loss: 0.6937 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6830 - loss: 0.6968 - val_accuracy: 0.7138 - val_loss: 0.6385
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7500 - loss: 0.6386
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6959 - loss: 0.7044 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6933 - loss: 0.7028 - val_accuracy: 0.6963 - val_loss: 0.6527
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7656 - loss: 0.5668
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6917 - loss: 0.6870 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6872 - loss: 0.6920 - val_accuracy: 0.7103 - val_loss: 0.6514
Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6484 - loss: 0.7180
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6761 - loss: 0.7070 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6806 - loss: 0.6989 - val_accuracy: 0.7026 - val_loss: 0.6429
Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7266 - loss: 0.6103
[1m33/78[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6856 - loss: 0.6847 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.6914 - loss: 0.6855
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6915 - loss: 0.6854 - val_accuracy: 0.6921 - val_loss: 0.6708
Epoch 17/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6562 - loss: 0.6850
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6981 - loss: 0.6628 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6973 - loss: 0.6675 - val_accuracy: 0.7061 - val_loss: 0.6535

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 363ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 2: 71.02 [%]
F1-score capturado en la ejecución 2: 70.56 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:35[0m 891ms/step
[1m 67/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 768us/step  
[1m135/310[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 754us/step
[1m205/310[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 743us/step
[1m275/310[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 736us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m72/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 705us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 862us/step
Global accuracy score (validation) = 71.17 [%]
Global F1 score (validation) = 71.65 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.5799165  0.3938957  0.01459881 0.01158906]
 [0.4617117  0.5278555  0.00167858 0.00875415]
 [0.520818   0.472264   0.00169327 0.0052248 ]
 ...
 [0.00267725 0.00407154 0.00411888 0.98913234]
 [0.0028353  0.00426492 0.00500639 0.9878934 ]
 [0.0113233  0.00989447 0.9546462  0.02413603]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.45 [%]
Global accuracy score (test) = 72.28 [%]
Global F1 score (train) = 74.35 [%]
Global F1 score (test) = 72.9 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.52      0.69      0.59       350
MODERATE-INTENSITY       0.53      0.40      0.46       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.95      0.86      0.90       299

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

2025-11-05 10:20:17.901117: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:20:17.912417: 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 00:00:1762334417.925723 2863403 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334417.929640 2863403 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334417.939636 2863403 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334417.939653 2863403 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334417.939654 2863403 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334417.939655 2863403 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:20:17.942750: 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 00:00:1762334420.170762 2863403 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334421.569364 2863517 service.cc:152] XLA service 0x7f143c00a710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334421.569396 2863517 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:20:21.606134: 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 00:00:1762334421.725879 2863517 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334424.098266 2863517 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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[1m34/78[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 2.0560 
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Epoch 2/108

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Epoch 3/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0381 
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Epoch 4/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5791 - loss: 0.9553 
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Epoch 5/108

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Epoch 6/108

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Epoch 7/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6534 - loss: 0.7934 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6172 - loss: 0.7782
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6585 - loss: 0.7642 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6585 - loss: 0.7685 - val_accuracy: 0.6984 - val_loss: 0.6326
Epoch 9/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6677 - loss: 0.7663 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6691 - loss: 0.7588 - val_accuracy: 0.7022 - val_loss: 0.6153
Epoch 10/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6790 - loss: 0.7343 
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Epoch 11/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6861 - loss: 0.7186 
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Epoch 12/108

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Epoch 13/108

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Epoch 14/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6886 - loss: 0.7115 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 385ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 3: 72.28 [%]
F1-score capturado en la ejecución 3: 72.9 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:25[0m 858ms/step
[1m 70/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 736us/step  
[1m141/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 722us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m70/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 729us/step
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Global accuracy score (validation) = 71.31 [%]
Global F1 score (validation) = 71.96 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.51244265 0.48325223 0.00135783 0.00294727]
 [0.5555654  0.4331665  0.00680165 0.00446638]
 [0.5407921  0.40309373 0.04007247 0.01604168]
 ...
 [0.00910058 0.01562176 0.00863157 0.9666461 ]
 [0.00523378 0.00902206 0.00593639 0.9798078 ]
 [0.01030294 0.00986255 0.9502846  0.02954984]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.07 [%]
Global accuracy score (test) = 73.83 [%]
Global F1 score (train) = 73.99 [%]
Global F1 score (test) = 74.53 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.54      0.69      0.61       350
MODERATE-INTENSITY       0.57      0.46      0.51       350
         SEDENTARY       0.96      0.96      0.96       350
VIGOROUS-INTENSITY       0.96      0.86      0.90       299

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

2025-11-05 10:20:40.427713: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:20:40.439259: 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 00:00:1762334440.452269 2865601 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334440.456390 2865601 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334440.466130 2865601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334440.466147 2865601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334440.466149 2865601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334440.466150 2865601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:20:40.469278: 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 00:00:1762334442.681075 2865601 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334444.048522 2865731 service.cc:152] XLA service 0x750c3000a3d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334444.048555 2865731 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:20:44.083774: 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 00:00:1762334444.202672 2865731 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334446.558003 2865731 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:10[0m 3s/step - accuracy: 0.2188 - loss: 2.5029
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 2.0043 
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Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1330
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4621 - loss: 1.1983 
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Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0081
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5474 - loss: 1.0311 
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9305
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9456
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8369
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6367 - loss: 0.8150 
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Epoch 7/108

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Epoch 8/108

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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.6250 - loss: 0.6997
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6619 - loss: 0.7602 
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6953 - loss: 0.6524
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6798 - loss: 0.7269 
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Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7891 - loss: 0.5365
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6880 - loss: 0.7173 
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Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7500 - loss: 0.6503
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6873 - loss: 0.7151 
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Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7344 - loss: 0.5875
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7027 - loss: 0.6914 
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Epoch 14/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6975 - loss: 0.7158 
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Epoch 15/108

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Epoch 16/108

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Epoch 17/108

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Epoch 18/108

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 387ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 4: 73.83 [%]
F1-score capturado en la ejecución 4: 74.53 [%]

=== 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}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:31[0m 877ms/step
[1m 70/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 733us/step  
[1m138/310[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 736us/step
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[1m267/310[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 758us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 738us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 861us/step
Global accuracy score (validation) = 70.33 [%]
Global F1 score (validation) = 70.62 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.1498443e-01 4.7127387e-01 6.4991820e-03 7.2424691e-03]
 [4.4521898e-01 5.4381710e-01 2.7890550e-03 8.1749111e-03]
 [4.9186474e-01 4.9709356e-01 4.4542821e-03 6.5873698e-03]
 ...
 [1.7581552e-03 3.9079715e-03 2.2167219e-03 9.9211711e-01]
 [8.4736542e-04 1.9423412e-03 9.4641978e-04 9.9626380e-01]
 [3.2382458e-02 2.6433039e-02 8.4266275e-01 9.8521687e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 73.82 [%]
Global accuracy score (test) = 74.72 [%]
Global F1 score (train) = 73.39 [%]
Global F1 score (test) = 75.1 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.61      0.42      0.50       350
MODERATE-INTENSITY       0.54      0.74      0.63       350
         SEDENTARY       0.97      0.95      0.96       350
VIGOROUS-INTENSITY       0.95      0.89      0.92       299

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

2025-11-05 10:21:03.838763: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:21:03.849723: 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 00:00:1762334463.862911 2868171 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334463.866993 2868171 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334463.876925 2868171 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334463.876943 2868171 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334463.876944 2868171 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334463.876952 2868171 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:21:03.879904: 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 00:00:1762334466.073791 2868171 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334467.443200 2868273 service.cc:152] XLA service 0x7e94a0009360 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334467.443227 2868273 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:21:07.478769: 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 00:00:1762334467.600576 2868273 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334469.982797 2868273 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 2/108

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Epoch 3/108

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Epoch 4/108

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Epoch 5/108

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Epoch 6/108

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Epoch 7/108

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Epoch 8/108

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Epoch 9/108

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Epoch 10/108

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Epoch 11/108

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Epoch 12/108

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Epoch 13/108

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Epoch 14/108

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Epoch 15/108

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Epoch 16/108

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Epoch 17/108

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Epoch 18/108

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Epoch 19/108

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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6932 - loss: 0.6862 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 364ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 5: 74.72 [%]
F1-score capturado en la ejecución 5: 75.1 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:29[0m 872ms/step
[1m 65/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 783us/step  
[1m139/310[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 727us/step
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[1m273/310[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 738us/step
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[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m68/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 753us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 876us/step
Global accuracy score (validation) = 70.86 [%]
Global F1 score (validation) = 71.78 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.8191162e-01 3.9009252e-01 1.5535626e-02 1.2460264e-02]
 [4.0949905e-01 5.8604312e-01 5.9404410e-04 3.8637498e-03]
 [4.0387297e-01 5.8818060e-01 8.2429417e-04 7.1221795e-03]
 ...
 [4.3113548e-03 8.1250817e-03 4.0661031e-03 9.8349750e-01]
 [1.6340099e-03 3.6026917e-03 9.0525590e-04 9.9385804e-01]
 [2.5297811e-02 2.3307232e-02 7.3621029e-01 2.1518473e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 76.23 [%]
Global accuracy score (test) = 72.65 [%]
Global F1 score (train) = 76.34 [%]
Global F1 score (test) = 73.72 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.53      0.61      0.56       350
MODERATE-INTENSITY       0.52      0.49      0.50       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.96      0.86      0.91       299

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

2025-11-05 10:21:27.473093: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:21:27.484374: 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 00:00:1762334487.497847 2870818 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334487.501875 2870818 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334487.511977 2870818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334487.511995 2870818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334487.511996 2870818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334487.511997 2870818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:21:27.514984: 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 00:00:1762334489.724391 2870818 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334491.114637 2870951 service.cc:152] XLA service 0x7ea878009dd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334491.114686 2870951 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:21:31.157085: 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 00:00:1762334491.277971 2870951 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334493.665035 2870951 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 2/108

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Epoch 3/108

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Epoch 4/108

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Epoch 5/108

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Epoch 6/108

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Epoch 7/108

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Epoch 8/108

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Epoch 9/108

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Epoch 10/108

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Epoch 11/108

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Epoch 12/108

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Epoch 13/108

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Epoch 14/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6925 - loss: 0.6883 
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Epoch 15/108

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Epoch 16/108

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Epoch 17/108

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[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7067 - loss: 0.6740 - val_accuracy: 0.7110 - val_loss: 0.6252
Epoch 18/108

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 380ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 6: 72.65 [%]
F1-score capturado en la ejecución 6: 73.72 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:19[0m 840ms/step
[1m 68/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 753us/step  
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[1m280/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 723us/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m70/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 726us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 845us/step
Global accuracy score (validation) = 72.12 [%]
Global F1 score (validation) = 73.31 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.60246927 0.3831867  0.00875592 0.00558809]
 [0.5442057  0.45197922 0.0012858  0.00252929]
 [0.4976721  0.49820492 0.00130906 0.00281388]
 ...
 [0.00412235 0.00729391 0.00674321 0.98184055]
 [0.00184867 0.00346598 0.00449264 0.99019265]
 [0.01250489 0.01088588 0.91965294 0.05695627]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.68 [%]
Global accuracy score (test) = 72.28 [%]
Global F1 score (train) = 76.06 [%]
Global F1 score (test) = 73.42 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.52      0.64      0.58       350
MODERATE-INTENSITY       0.52      0.48      0.50       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.97      0.83      0.89       299

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

2025-11-05 10:21:50.742994: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:21:50.754171: 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 00:00:1762334510.767317 2873391 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334510.771433 2873391 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334510.781077 2873391 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334510.781094 2873391 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334510.781096 2873391 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334510.781097 2873391 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:21:50.784180: 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 00:00:1762334512.984088 2873391 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334514.334879 2873516 service.cc:152] XLA service 0x7bab78009ce0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334514.334908 2873516 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:21:54.368321: 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 00:00:1762334514.489890 2873516 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334516.877387 2873516 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 2/108

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Epoch 3/108

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Epoch 4/108

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Epoch 5/108

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Epoch 6/108

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Epoch 7/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6464 - loss: 0.8093 
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Epoch 8/108

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Epoch 9/108

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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6722 - loss: 0.7614 
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Epoch 10/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6683 - loss: 0.7316 
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Epoch 11/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6778 - loss: 0.7227 
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Epoch 12/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6827 - loss: 0.7216 
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Epoch 13/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6837 - loss: 0.7040 
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Epoch 14/108

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Epoch 15/108

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Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6719 - loss: 0.7946
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6981 - loss: 0.7049 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6959 - loss: 0.6959 - val_accuracy: 0.7019 - val_loss: 0.6349
Epoch 17/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7344 - loss: 0.6506
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7037 - loss: 0.6974 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7023 - loss: 0.6923 - val_accuracy: 0.7131 - val_loss: 0.6293
Epoch 18/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6562 - loss: 0.6616
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7027 - loss: 0.6569 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7067 - loss: 0.6622 - val_accuracy: 0.7142 - val_loss: 0.6285
Epoch 19/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6719 - loss: 0.8249
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7148 - loss: 0.6740 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7118 - loss: 0.6716 - val_accuracy: 0.7191 - val_loss: 0.6214
Epoch 20/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7656 - loss: 0.5468
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7095 - loss: 0.6666 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7101 - loss: 0.6665 - val_accuracy: 0.7138 - val_loss: 0.6344
Epoch 21/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6719 - loss: 0.7253
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7077 - loss: 0.6483 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7122 - loss: 0.6499 - val_accuracy: 0.7051 - val_loss: 0.6323
Epoch 22/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7656 - loss: 0.6074
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7185 - loss: 0.6574 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.7179 - loss: 0.6587
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7180 - loss: 0.6586 - val_accuracy: 0.7135 - val_loss: 0.6549
Epoch 23/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7344 - loss: 0.5671
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7278 - loss: 0.6423 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7233 - loss: 0.6494 - val_accuracy: 0.7170 - val_loss: 0.6327
Epoch 24/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7031 - loss: 0.5815
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7148 - loss: 0.6336 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7165 - loss: 0.6422 - val_accuracy: 0.7247 - val_loss: 0.6260

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 386ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 7: 72.28 [%]
F1-score capturado en la ejecución 7: 73.42 [%]

=== 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)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:22[0m 851ms/step
[1m 74/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 688us/step  
[1m149/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 680us/step
[1m219/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 693us/step
[1m285/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 710us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 794us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 889us/step
Global accuracy score (validation) = 72.33 [%]
Global F1 score (validation) = 73.13 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[6.0422486e-01 3.9265680e-01 1.8908300e-03 1.2274870e-03]
 [5.9163207e-01 4.0056548e-01 4.4878735e-03 3.3145722e-03]
 [3.6720952e-01 6.2433577e-01 1.7466791e-03 6.7080595e-03]
 ...
 [2.6457647e-03 4.5717177e-03 5.5879881e-03 9.8719448e-01]
 [4.5162559e-04 1.1029580e-03 6.3298875e-04 9.9781239e-01]
 [2.0869670e-02 1.7866299e-02 7.9360652e-01 1.6765758e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 77.54 [%]
Global accuracy score (test) = 74.57 [%]
Global F1 score (train) = 77.67 [%]
Global F1 score (test) = 75.49 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.54      0.68      0.60       350
MODERATE-INTENSITY       0.58      0.50      0.54       350
         SEDENTARY       0.98      0.95      0.96       350
VIGOROUS-INTENSITY       0.96      0.88      0.91       299

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

2025-11-05 10:22:15.196931: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:22:15.208349: 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 00:00:1762334535.221329 2876500 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334535.225218 2876500 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334535.235067 2876500 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334535.235083 2876500 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334535.235091 2876500 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334535.235092 2876500 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:22:15.238205: 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 00:00:1762334537.445936 2876500 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334538.836412 2876610 service.cc:152] XLA service 0x73e5bc009290 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334538.836445 2876610 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:22:18.878253: 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 00:00:1762334538.998886 2876610 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334541.403186 2876610 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:15[0m 3s/step - accuracy: 0.2578 - loss: 2.8344
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3360 - loss: 2.1627 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.3625 - loss: 1.9609
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Epoch 2/108

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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.2382 
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Epoch 3/108

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[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0657 
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Epoch 4/108

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[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6026 - loss: 0.9148 
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Epoch 5/108

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Epoch 6/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6528 - loss: 0.7972 
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Epoch 7/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6627 - loss: 0.7723 
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Epoch 8/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6540 - loss: 0.7627 
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Epoch 9/108

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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6737 - loss: 0.7378 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6740 - loss: 0.7428
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Epoch 10/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6862 - loss: 0.7183 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6860 - loss: 0.7240 - val_accuracy: 0.7037 - val_loss: 0.6605
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7656 - loss: 0.6093
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6842 - loss: 0.7183 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6827 - loss: 0.7213 - val_accuracy: 0.6777 - val_loss: 0.6955
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7188 - loss: 0.7561
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6917 - loss: 0.7095 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6916 - loss: 0.7106 - val_accuracy: 0.7131 - val_loss: 0.6294
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7087
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6948 - loss: 0.7035 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6927 - loss: 0.7071 - val_accuracy: 0.6966 - val_loss: 0.6341
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7266 - loss: 0.6737
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6980 - loss: 0.7098 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6973 - loss: 0.7115 - val_accuracy: 0.6973 - val_loss: 0.6514
Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7266 - loss: 0.7327
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7077 - loss: 0.6619 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7060 - loss: 0.6675 - val_accuracy: 0.7121 - val_loss: 0.6433
Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.7188 - loss: 0.6457
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7023 - loss: 0.6681 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7053 - loss: 0.6677 - val_accuracy: 0.7072 - val_loss: 0.6362
Epoch 17/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6484 - loss: 0.7816
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7080 - loss: 0.6845 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7064 - loss: 0.6839 - val_accuracy: 0.7100 - val_loss: 0.6725

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 371ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Saved model to disk.

Accuracy capturado en la ejecución 8: 74.57 [%]
F1-score capturado en la ejecución 8: 75.49 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:25[0m 858ms/step
[1m 61/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 835us/step  
[1m130/310[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 783us/step
[1m202/310[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 754us/step
[1m278/310[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 729us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m70/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 734us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 865us/step
Global accuracy score (validation) = 71.31 [%]
Global F1 score (validation) = 72.29 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.44738898 0.5363939  0.00303864 0.01317855]
 [0.45984444 0.53472376 0.00112214 0.00430974]
 [0.56836903 0.4249693  0.00312077 0.00354098]
 ...
 [0.00207115 0.00578747 0.00209537 0.99004596]
 [0.00124346 0.00375025 0.00101488 0.9939913 ]
 [0.05536978 0.0482909  0.8357969  0.06054244]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.5 [%]
Global accuracy score (test) = 71.91 [%]
Global F1 score (train) = 75.84 [%]
Global F1 score (test) = 72.98 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.57      0.54       350
MODERATE-INTENSITY       0.52      0.50      0.51       350
         SEDENTARY       0.95      0.97      0.96       350
VIGOROUS-INTENSITY       0.97      0.86      0.91       299

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

2025-11-05 10:22:38.441098: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:22:38.452057: 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 00:00:1762334558.465378 2878965 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334558.469266 2878965 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334558.479180 2878965 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334558.479196 2878965 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334558.479198 2878965 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334558.479199 2878965 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:22:38.482166: 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 00:00:1762334560.671677 2878965 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334562.053252 2879097 service.cc:152] XLA service 0x7854f80095f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334562.053287 2879097 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:22:42.091272: 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 00:00:1762334562.212269 2879097 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334564.743012 2879097 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:24[0m 3s/step - accuracy: 0.2891 - loss: 2.7450
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 2.0861 
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Epoch 2/108

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Epoch 3/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0439 
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Epoch 4/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5851 - loss: 0.9221 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5883 - loss: 0.9195 - val_accuracy: 0.6650 - val_loss: 0.6962
Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.5703 - loss: 0.8918
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6054 - loss: 0.8871 
[1m76/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6121 - loss: 0.8793
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6123 - loss: 0.8789 - val_accuracy: 0.6959 - val_loss: 0.6735
Epoch 6/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6491 - loss: 0.7945 
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6953 - loss: 0.7392
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6629 - loss: 0.7811 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6875 - loss: 0.7288
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6557 - loss: 0.7569 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6556 - loss: 0.7661 - val_accuracy: 0.6945 - val_loss: 0.6588
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.6328 - loss: 0.7657
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6617 - loss: 0.7598 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6658 - loss: 0.7586 - val_accuracy: 0.7012 - val_loss: 0.6271
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 26ms/step - accuracy: 0.7109 - loss: 0.6870
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6692 - loss: 0.7382 
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Epoch 11/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6732 - loss: 0.7134 
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Epoch 12/108

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Epoch 13/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6833 - loss: 0.7097 
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Epoch 14/108

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Epoch 15/108

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 421ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 9: 71.91 [%]
F1-score capturado en la ejecución 9: 72.98 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:25[0m 859ms/step
[1m 70/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 728us/step  
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[1m68/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 749us/step
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Global accuracy score (validation) = 70.65 [%]
Global F1 score (validation) = 71.56 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.43949124 0.54141176 0.00197776 0.01711921]
 [0.5713071  0.40927473 0.01070844 0.00870974]
 [0.43303382 0.5605086  0.00137388 0.00508366]
 ...
 [0.00423952 0.00730322 0.00661111 0.9818461 ]
 [0.0013606  0.00223946 0.00273235 0.99366754]
 [0.01016227 0.00880464 0.95501333 0.02601969]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.51 [%]
Global accuracy score (test) = 72.79 [%]
Global F1 score (train) = 74.87 [%]
Global F1 score (test) = 73.82 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.55      0.51      0.53       350
MODERATE-INTENSITY       0.52      0.62      0.57       350
         SEDENTARY       0.96      0.95      0.96       350
VIGOROUS-INTENSITY       0.95      0.85      0.90       299

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

2025-11-05 10:23:01.755681: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:23:01.766984: 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 00:00:1762334581.780012 2882190 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334581.783954 2882190 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334581.793971 2882190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334581.793990 2882190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334581.793992 2882190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334581.793994 2882190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:23:01.797023: 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 00:00:1762334584.036285 2882190 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334585.418050 2882323 service.cc:152] XLA service 0x715724009320 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334585.418087 2882323 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:23:05.454970: 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 00:00:1762334585.574530 2882323 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334588.004640 2882323 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 2/108

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Epoch 3/108

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Epoch 4/108

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Epoch 5/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5881 - loss: 0.8813 
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Epoch 6/108

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Epoch 7/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6551 - loss: 0.8083 
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Epoch 8/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6599 - loss: 0.7917 
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Epoch 9/108

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[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6708 - loss: 0.7461 
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Epoch 10/108

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Epoch 11/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6875 - loss: 0.7222 
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Epoch 12/108

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Epoch 13/108

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Epoch 14/108

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Epoch 15/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6938 - loss: 0.6954 
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Saved model to disk.

Accuracy capturado en la ejecución 10: 72.79 [%]
F1-score capturado en la ejecución 10: 73.82 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:33[0m 886ms/step
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[1m277/310[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 731us/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m73/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 699us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 824us/step
Global accuracy score (validation) = 70.44 [%]
Global F1 score (validation) = 71.32 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.5483418  0.43552944 0.00780001 0.00832868]
 [0.5253024  0.43572068 0.01990438 0.01907255]
 [0.43984857 0.5450095  0.00287544 0.01226652]
 ...
 [0.00442289 0.00823444 0.0072215  0.98012125]
 [0.00171251 0.003628   0.00190264 0.9927569 ]
 [0.01535647 0.01304874 0.9418331  0.02976175]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.07 [%]
Global accuracy score (test) = 72.87 [%]
Global F1 score (train) = 74.3 [%]
Global F1 score (test) = 73.82 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.56      0.47      0.51       350
MODERATE-INTENSITY       0.51      0.65      0.57       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.96      0.85      0.90       299

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

2025-11-05 10:23:24.687881: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:23:24.699270: 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 00:00:1762334604.712433 2884511 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334604.716356 2884511 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334604.726405 2884511 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334604.726423 2884511 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334604.726425 2884511 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334604.726427 2884511 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:23:24.729656: 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 00:00:1762334606.951531 2884511 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334608.365189 2884626 service.cc:152] XLA service 0x730f8401b710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334608.365220 2884626 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:23:28.398589: 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 00:00:1762334608.519127 2884626 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334610.939711 2884626 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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[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 2.1616 
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Epoch 2/108

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Epoch 3/108

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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6094 - loss: 0.8652
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8936
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9223
[1m36/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6233 - loss: 0.8473 
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7341
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6502 - loss: 0.7834 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6172 - loss: 0.8560
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6547 - loss: 0.7876 
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6953 - loss: 0.7729
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6723 - loss: 0.7439 
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6641 - loss: 0.6975
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6818 - loss: 0.7272 
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Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.6953 - loss: 0.6858
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6788 - loss: 0.7053 
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Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6484 - loss: 0.7557
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6775 - loss: 0.7341 
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Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7451
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6871 - loss: 0.6952 
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Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7041
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6861 - loss: 0.7112 
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Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.6875 - loss: 0.7649
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7038 - loss: 0.7038 
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Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7109 - loss: 0.7245
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6958 - loss: 0.6985 
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Epoch 17/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7344 - loss: 0.6652
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7116 - loss: 0.6636 
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Epoch 18/108

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Epoch 19/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7148 - loss: 0.6655 
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Epoch 20/108

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Epoch 21/108

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Epoch 22/108

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[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7344 - loss: 0.6467 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 368ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 11: 72.87 [%]
F1-score capturado en la ejecución 11: 73.82 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:30[0m 875ms/step
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[1m226/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 672us/step
[1m299/310[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 676us/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m64/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 802us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 917us/step
Global accuracy score (validation) = 71.52 [%]
Global F1 score (validation) = 72.77 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.97211242e-01 3.96150589e-01 3.62431980e-03 3.01388348e-03]
 [5.26036203e-01 4.69003320e-01 1.85675325e-03 3.10379104e-03]
 [5.98867357e-01 3.74050438e-01 1.60520673e-02 1.10301189e-02]
 ...
 [1.93864072e-03 5.52081969e-03 1.59426709e-03 9.90946293e-01]
 [1.93527865e-03 7.46211410e-03 7.31198292e-04 9.89871442e-01]
 [2.12990940e-02 1.16061745e-02 9.42141652e-01 2.49530505e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 77.09 [%]
Global accuracy score (test) = 71.76 [%]
Global F1 score (train) = 77.51 [%]
Global F1 score (test) = 73.05 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.54      0.54      0.54       350
MODERATE-INTENSITY       0.49      0.56      0.53       350
         SEDENTARY       0.97      0.96      0.97       350
VIGOROUS-INTENSITY       0.97      0.82      0.89       299

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

2025-11-05 10:23:48.888715: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:23:48.899779: 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 00:00:1762334628.912764 2887442 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334628.916890 2887442 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334628.927566 2887442 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334628.927584 2887442 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334628.927585 2887442 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334628.927586 2887442 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:23:48.930911: 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 00:00:1762334631.149550 2887442 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334632.525201 2887573 service.cc:152] XLA service 0x73a90c0096e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334632.525225 2887573 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:23:52.558526: 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 00:00:1762334632.680074 2887573 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334635.041440 2887573 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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[1m27/78[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3388 - loss: 2.2339 
[1m68/78[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3578 - loss: 1.9767
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Epoch 2/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.2205 
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Epoch 3/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0374 
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5859 - loss: 0.9422
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5864 - loss: 0.9412 
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Epoch 5/108

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[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6130 - loss: 0.8600 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6179 - loss: 0.8643
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6182 - loss: 0.8644 - val_accuracy: 0.6784 - val_loss: 0.6740
Epoch 6/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6353 - loss: 0.8207 
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7031 - loss: 0.7905
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6543 - loss: 0.7940 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6556 - loss: 0.7904 - val_accuracy: 0.7015 - val_loss: 0.6789
Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6719 - loss: 0.8166
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6666 - loss: 0.7786 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6667 - loss: 0.7718 - val_accuracy: 0.6756 - val_loss: 0.6981
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.6719 - loss: 0.8997
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6742 - loss: 0.7618 
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Epoch 10/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6883 - loss: 0.7161 
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Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7023
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6912 - loss: 0.7016 
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Epoch 12/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6901 - loss: 0.7219 
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Epoch 13/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6894 - loss: 0.7014 
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Epoch 14/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7080 - loss: 0.6852 
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Epoch 15/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6953 - loss: 0.7140 
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Epoch 16/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6993 - loss: 0.6773 
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Epoch 17/108

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Epoch 18/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7121 - loss: 0.6824 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 372ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 12: 71.76 [%]
F1-score capturado en la ejecución 12: 73.05 [%]

=== 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}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:24[0m 855ms/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 773us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 934us/step
Global accuracy score (validation) = 70.82 [%]
Global F1 score (validation) = 71.45 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[4.5091546e-01 5.4186743e-01 2.3852652e-03 4.8318896e-03]
 [4.0932962e-01 5.7144117e-01 4.7735679e-03 1.4455659e-02]
 [3.8056377e-01 6.1621457e-01 6.4356253e-04 2.5781272e-03]
 ...
 [7.3449843e-04 1.8275555e-03 1.5243782e-03 9.9591345e-01]
 [4.4598049e-04 1.0426848e-03 1.6668993e-03 9.9684447e-01]
 [1.1997842e-02 8.1175920e-03 9.5132083e-01 2.8563816e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.56 [%]
Global accuracy score (test) = 73.09 [%]
Global F1 score (train) = 74.78 [%]
Global F1 score (test) = 74.01 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.56      0.47      0.51       350
MODERATE-INTENSITY       0.52      0.65      0.58       350
         SEDENTARY       0.96      0.97      0.96       350
VIGOROUS-INTENSITY       0.97      0.85      0.91       299

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

2025-11-05 10:24:12.180926: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:24:12.192472: 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 00:00:1762334652.205706 2890015 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334652.209775 2890015 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334652.219450 2890015 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334652.219467 2890015 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334652.219468 2890015 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334652.219470 2890015 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:24:12.222623: 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 00:00:1762334654.452630 2890015 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334655.811344 2890145 service.cc:152] XLA service 0x7419bc01b5c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334655.811376 2890145 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:24:15.846700: 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 00:00:1762334655.963268 2890145 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334658.360257 2890145 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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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3183 - loss: 2.2416 
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Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.4375 - loss: 1.3547
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.2163 
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Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 26ms/step - accuracy: 0.5391 - loss: 1.0619
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5447 - loss: 1.0303 
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.5625 - loss: 0.9679
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5804 - loss: 0.9391 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5860 - loss: 0.9275 - val_accuracy: 0.6843 - val_loss: 0.7123
Epoch 5/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6131 - loss: 0.8821 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6171 - loss: 0.8707 - val_accuracy: 0.6724 - val_loss: 0.6773
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6953 - loss: 0.8129
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6341 - loss: 0.8235 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6344 - loss: 0.8198 - val_accuracy: 0.6945 - val_loss: 0.6516
Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7031 - loss: 0.8058
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6605 - loss: 0.7830 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8098
[1m36/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6553 - loss: 0.7786 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.6598 - loss: 0.7681
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6598 - loss: 0.7680 - val_accuracy: 0.6864 - val_loss: 0.6442
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7109 - loss: 0.6174
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6689 - loss: 0.7140 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6668 - loss: 0.7278 - val_accuracy: 0.7015 - val_loss: 0.6484
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6484 - loss: 0.8204
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6708 - loss: 0.7548 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6736 - loss: 0.7437 - val_accuracy: 0.7072 - val_loss: 0.6313
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.7409
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6832 - loss: 0.7156 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6814 - loss: 0.7207 - val_accuracy: 0.7075 - val_loss: 0.6460
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7188 - loss: 0.6693
[1m36/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6811 - loss: 0.7136 
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Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6875 - loss: 0.7590
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6829 - loss: 0.7236 
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Epoch 14/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7015 - loss: 0.6937 
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Epoch 15/108

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Saved model to disk.

Accuracy capturado en la ejecución 13: 73.09 [%]
F1-score capturado en la ejecución 13: 74.01 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

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[1m278/310[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 728us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m76/89[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 679us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 811us/step
Global accuracy score (validation) = 70.47 [%]
Global F1 score (validation) = 71.41 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.558769   0.42928144 0.00585819 0.00609139]
 [0.45752916 0.52382743 0.00289489 0.01574856]
 [0.5419563  0.44586527 0.00512873 0.00704967]
 ...
 [0.00975392 0.01501549 0.01999066 0.95524   ]
 [0.00497729 0.00904267 0.0057914  0.9801886 ]
 [0.01604968 0.01185396 0.94885135 0.02324505]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.32 [%]
Global accuracy score (test) = 73.09 [%]
Global F1 score (train) = 75.74 [%]
Global F1 score (test) = 74.12 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.55      0.52      0.54       350
MODERATE-INTENSITY       0.53      0.63      0.58       350
         SEDENTARY       0.96      0.97      0.96       350
VIGOROUS-INTENSITY       0.97      0.82      0.89       299

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

2025-11-05 10:24:35.006338: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:24:35.017756: 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 00:00:1762334675.031162 2892317 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334675.035368 2892317 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334675.045426 2892317 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334675.045443 2892317 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334675.045445 2892317 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334675.045446 2892317 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:24:35.048392: 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 00:00:1762334677.268569 2892317 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334678.652089 2892444 service.cc:152] XLA service 0x7d0bec009640 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334678.652129 2892444 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:24:38.687753: 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 00:00:1762334678.807230 2892444 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334681.204666 2892444 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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[1m31/78[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3303 - loss: 2.3025 
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Epoch 2/108

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Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6016 - loss: 1.0036
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9437
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7031 - loss: 0.7651
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8233
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 0.7596
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6489 - loss: 0.7946 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6953 - loss: 0.7756
[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6601 - loss: 0.7807 
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7109 - loss: 0.7028
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6790 - loss: 0.7470 
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6172 - loss: 0.7995
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6829 - loss: 0.7293 
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Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 0.7111
[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6792 - loss: 0.7186 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6793 - loss: 0.7234 - val_accuracy: 0.7195 - val_loss: 0.6038
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.7405
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6724 - loss: 0.7341 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6771 - loss: 0.7302 - val_accuracy: 0.7145 - val_loss: 0.6105
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.5859 - loss: 0.7602
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6769 - loss: 0.7177 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6811 - loss: 0.7157 - val_accuracy: 0.7177 - val_loss: 0.5944
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6406 - loss: 0.6265
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6985 - loss: 0.6745 
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Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6953 - loss: 0.6714
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7002 - loss: 0.6717 
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Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7188 - loss: 0.6375
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7093 - loss: 0.6795 
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Epoch 17/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6875 - loss: 0.6377
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6999 - loss: 0.6950 
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Epoch 18/108

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Epoch 19/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7095 - loss: 0.6720 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 392ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 14: 73.09 [%]
F1-score capturado en la ejecución 14: 74.12 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:34[0m 887ms/step
[1m 69/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 740us/step  
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m80/89[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 639us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 784us/step
Global accuracy score (validation) = 71.1 [%]
Global F1 score (validation) = 71.12 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.61714166 0.37409145 0.00463722 0.00412969]
 [0.60114264 0.37989378 0.01086941 0.00809417]
 [0.5144994  0.48007935 0.00120681 0.00421443]
 ...
 [0.0054138  0.0090234  0.01135791 0.97420484]
 [0.00290625 0.0049231  0.00816778 0.9840029 ]
 [0.01522432 0.01109034 0.9497855  0.02389983]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.51 [%]
Global accuracy score (test) = 75.83 [%]
Global F1 score (train) = 75.22 [%]
Global F1 score (test) = 76.02 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.57      0.80      0.66       350
MODERATE-INTENSITY       0.63      0.43      0.51       350
         SEDENTARY       0.96      0.97      0.96       350
VIGOROUS-INTENSITY       0.96      0.85      0.90       299

          accuracy                           0.76      1349
         macro avg       0.78      0.76      0.76      1349
      weighted avg       0.77      0.76      0.75      1349

2025-11-05 10:24:58.541326: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:24:58.552538: 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 00:00:1762334698.565812 2894975 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334698.569943 2894975 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334698.579745 2894975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334698.579761 2894975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334698.579762 2894975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334698.579763 2894975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:24:58.582951: 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 00:00:1762334700.817102 2894975 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334702.195787 2895102 service.cc:152] XLA service 0x7a96fc009440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334702.195829 2895102 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:25:02.231028: 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 00:00:1762334702.352010 2895102 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334704.765583 2895102 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:15[0m 3s/step - accuracy: 0.1172 - loss: 3.0588
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3239 - loss: 2.0939 
[1m76/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3538 - loss: 1.8800
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Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.4844 - loss: 1.1679
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1403 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1285
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4926 - loss: 1.1282 - val_accuracy: 0.6436 - val_loss: 0.8104
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1401
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0289 
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6875 - loss: 0.9037
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6012 - loss: 0.9337 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5996 - loss: 0.9275 - val_accuracy: 0.6735 - val_loss: 0.7097
Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5469 - loss: 0.8616
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6083 - loss: 0.8744 
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6016 - loss: 0.9618
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6451 - loss: 0.8208 
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6172 - loss: 0.9716
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6359 - loss: 0.8223 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6875 - loss: 0.7130
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6599 - loss: 0.7653 
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6484 - loss: 0.6694
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6566 - loss: 0.7567 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6566 - loss: 0.7553
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7500 - loss: 0.7234
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7025 - loss: 0.7337 
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Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7188 - loss: 0.6859
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6706 - loss: 0.7450 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6727 - loss: 0.7407 - val_accuracy: 0.6784 - val_loss: 0.6555
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6875 - loss: 0.5713
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6770 - loss: 0.7201 
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Epoch 13/108

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[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6834 - loss: 0.6874 
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Epoch 14/108

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[1m76/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6897 - loss: 0.7101
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 357ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 15: 75.83 [%]
F1-score capturado en la ejecución 15: 76.02 [%]

=== 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}
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)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:27[0m 866ms/step
[1m 70/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 727us/step  
[1m145/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 702us/step
[1m217/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 702us/step
[1m292/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 694us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[1m71/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 718us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 865us/step
Global accuracy score (validation) = 70.4 [%]
Global F1 score (validation) = 67.68 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.49997443 0.48988912 0.00125274 0.0088837 ]
 [0.5620714  0.40230858 0.02133689 0.01428324]
 [0.5579451  0.4290255  0.00580242 0.00722701]
 ...
 [0.00973334 0.01508589 0.02053836 0.9546424 ]
 [0.00612154 0.00971984 0.01225615 0.9719025 ]
 [0.01698141 0.01673065 0.9378743  0.02841361]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 71.26 [%]
Global accuracy score (test) = 72.28 [%]
Global F1 score (train) = 67.9 [%]
Global F1 score (test) = 70.31 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.87      0.64       350
MODERATE-INTENSITY       0.57      0.20      0.29       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.93      0.88      0.91       299

          accuracy                           0.72      1349
         macro avg       0.75      0.73      0.70      1349
      weighted avg       0.74      0.72      0.70      1349

2025-11-05 10:25:21.109150: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:25:21.120316: 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 00:00:1762334721.133700 2897198 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334721.137741 2897198 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334721.147914 2897198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334721.147931 2897198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334721.147932 2897198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334721.147933 2897198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:25:21.151058: 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 00:00:1762334723.418938 2897198 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334724.791788 2897309 service.cc:152] XLA service 0x7232dc01b160 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334724.791823 2897309 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:25:24.826163: 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 00:00:1762334724.954215 2897309 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334727.385379 2897309 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:17[0m 3s/step - accuracy: 0.2188 - loss: 2.7601
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3251 - loss: 2.1450 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.3547 - loss: 1.9399
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 51ms/step - accuracy: 0.3553 - loss: 1.9359 - val_accuracy: 0.5372 - val_loss: 0.8901
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2595
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.2166 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1911
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4771 - loss: 1.1904 - val_accuracy: 0.6127 - val_loss: 0.8197
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4766 - loss: 1.0974
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0385 
[1m74/78[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0240
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0202
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5690 - loss: 0.9615 
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6016 - loss: 0.9546
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6090 - loss: 0.8745 
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8009
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7031 - loss: 0.7777
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6551 - loss: 0.7818 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 0.7274
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6624 - loss: 0.7562 
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8728
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6601 - loss: 0.7680 
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6797 - loss: 0.7690
[1m35/78[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6732 - loss: 0.7346 
[1m76/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6714 - loss: 0.7316
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6714 - loss: 0.7317 - val_accuracy: 0.6917 - val_loss: 0.6429
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6641 - loss: 0.7786
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6724 - loss: 0.7312 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6745 - loss: 0.7287 - val_accuracy: 0.6868 - val_loss: 0.6525
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7109 - loss: 0.6621
[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6908 - loss: 0.6994 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6882 - loss: 0.7045 - val_accuracy: 0.7135 - val_loss: 0.6219
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 0.6845
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6676 - loss: 0.7037 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6748 - loss: 0.7046 - val_accuracy: 0.7135 - val_loss: 0.6223
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5703 - loss: 0.7687
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6767 - loss: 0.7062 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6826 - loss: 0.6996 - val_accuracy: 0.7005 - val_loss: 0.6302
Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7266 - loss: 0.6858
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7022 - loss: 0.6801 
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Epoch 16/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6808 - loss: 0.6870 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6834 - loss: 0.6851 - val_accuracy: 0.7103 - val_loss: 0.6444
Epoch 17/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6562 - loss: 0.6796
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6985 - loss: 0.6695 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6991 - loss: 0.6717 - val_accuracy: 0.7044 - val_loss: 0.6258

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 368ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 72.28 [%]
F1-score capturado en la ejecución 16: 70.31 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:27[0m 865ms/step
[1m 74/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 690us/step  
[1m146/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 694us/step
[1m219/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 693us/step
[1m291/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 693us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m71/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 721us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 854us/step
Global accuracy score (validation) = 69.98 [%]
Global F1 score (validation) = 69.88 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.0677872e-01 4.7886252e-01 6.7859185e-03 7.5728390e-03]
 [5.0833750e-01 4.7724220e-01 6.9722943e-03 7.4479557e-03]
 [3.7899080e-01 6.1092103e-01 7.9811784e-04 9.2900461e-03]
 ...
 [2.2188907e-03 3.6015671e-03 4.5519546e-03 9.8962760e-01]
 [1.2745678e-03 2.0671182e-03 2.7004238e-03 9.9395788e-01]
 [1.6954422e-02 1.2655024e-02 9.3561542e-01 3.4775171e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 72.83 [%]
Global accuracy score (test) = 74.28 [%]
Global F1 score (train) = 72.06 [%]
Global F1 score (test) = 74.3 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.63      0.37      0.46       350
MODERATE-INTENSITY       0.52      0.79      0.63       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.95      0.86      0.90       299

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

2025-11-05 10:25:44.350390: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:25:44.361927: 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 00:00:1762334744.375414 2899668 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334744.379817 2899668 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334744.390159 2899668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334744.390179 2899668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334744.390181 2899668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334744.390182 2899668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:25:44.393346: 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 00:00:1762334746.607113 2899668 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334747.972089 2899799 service.cc:152] XLA service 0x77573c004ad0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334747.972132 2899799 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:25:48.010011: 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 00:00:1762334748.126703 2899799 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334750.542734 2899799 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:14[0m 3s/step - accuracy: 0.2500 - loss: 2.5070
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.9608 
[1m76/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3675 - loss: 1.7898
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.3686 - loss: 1.7833
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 51ms/step - accuracy: 0.3691 - loss: 1.7802 - val_accuracy: 0.5948 - val_loss: 0.8799
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2467
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1633 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5040 - loss: 1.1453 - val_accuracy: 0.6201 - val_loss: 0.8033
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0509
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0541 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0359
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5411 - loss: 1.0350 - val_accuracy: 0.6352 - val_loss: 0.7378
Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.6484 - loss: 0.8653
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5922 - loss: 0.9472 
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6797 - loss: 0.8081
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6465 - loss: 0.8510 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6389 - loss: 0.8541 - val_accuracy: 0.6808 - val_loss: 0.6697
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7896
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6447 - loss: 0.8145 
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6562 - loss: 0.7307
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6570 - loss: 0.7779 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6016 - loss: 0.8530
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6455 - loss: 0.7988 
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6953 - loss: 0.7071
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6637 - loss: 0.7600 
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7266 - loss: 0.7030
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6775 - loss: 0.7388 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.6758 - loss: 0.7393
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6758 - loss: 0.7393 - val_accuracy: 0.7142 - val_loss: 0.6196
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.7266 - loss: 0.7018
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6942 - loss: 0.7146 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6916 - loss: 0.7140 - val_accuracy: 0.6882 - val_loss: 0.6536
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8224
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6770 - loss: 0.7193 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6805 - loss: 0.7171 - val_accuracy: 0.6871 - val_loss: 0.6451
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.7031 - loss: 0.7478
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6945 - loss: 0.7030 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6898 - loss: 0.7055 - val_accuracy: 0.7037 - val_loss: 0.6071
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.6719 - loss: 0.5988
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6947 - loss: 0.6790 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6970 - loss: 0.6821 - val_accuracy: 0.7022 - val_loss: 0.6471
Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.7422 - loss: 0.5778
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6998 - loss: 0.6854 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6971 - loss: 0.6900 - val_accuracy: 0.7008 - val_loss: 0.6407
Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.7344 - loss: 0.6835
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7090 - loss: 0.6844 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7082 - loss: 0.6854 - val_accuracy: 0.7166 - val_loss: 0.6137
Epoch 17/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7238 - loss: 0.6634 
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Epoch 18/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6875 - loss: 0.5776
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6998 - loss: 0.6708 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7002 - loss: 0.6750 - val_accuracy: 0.7079 - val_loss: 0.6336

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 368ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 17: 74.28 [%]
F1-score capturado en la ejecución 17: 74.3 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:22[0m 848ms/step
[1m 69/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 739us/step  
[1m143/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 709us/step
[1m213/310[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 713us/step
[1m284/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 712us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m63/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 808us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 71.56 [%]
Global F1 score (validation) = 72.4 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[4.1113669e-01 5.8134192e-01 8.8008889e-04 6.6412543e-03]
 [3.9312196e-01 5.9452599e-01 1.2658382e-03 1.1086219e-02]
 [4.6943119e-01 5.2569413e-01 1.0033573e-03 3.8713561e-03]
 ...
 [6.1122817e-03 1.1379711e-02 6.0489955e-03 9.7645903e-01]
 [1.4880608e-03 3.6958721e-03 9.0334978e-04 9.9391276e-01]
 [2.1521825e-02 1.5179644e-02 9.3224603e-01 3.1052560e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.8 [%]
Global accuracy score (test) = 73.61 [%]
Global F1 score (train) = 76.04 [%]
Global F1 score (test) = 74.66 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.54      0.61      0.57       350
MODERATE-INTENSITY       0.53      0.52      0.52       350
         SEDENTARY       0.98      0.98      0.98       350
VIGOROUS-INTENSITY       0.97      0.86      0.91       299

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

2025-11-05 10:26:07.618857: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:26:07.630056: 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 00:00:1762334767.643253 2902239 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334767.647186 2902239 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334767.657182 2902239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334767.657200 2902239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334767.657201 2902239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334767.657202 2902239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:26:07.660287: 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 00:00:1762334769.854881 2902239 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334771.245766 2902350 service.cc:152] XLA service 0x762c84017250 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334771.245794 2902350 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:26:11.279792: 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 00:00:1762334771.407177 2902350 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334773.772335 2902350 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:13[0m 3s/step - accuracy: 0.2734 - loss: 2.3382
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 2.0051 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 30ms/step - accuracy: 0.3637 - loss: 1.8375
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 52ms/step - accuracy: 0.3641 - loss: 1.8343 - val_accuracy: 0.5909 - val_loss: 0.9151
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1327
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4708 - loss: 1.1822 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1575 - val_accuracy: 0.6232 - val_loss: 0.8150
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5078 - loss: 1.1676
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0350 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5465 - loss: 1.0173 - val_accuracy: 0.6397 - val_loss: 0.7516
Epoch 4/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6051 - loss: 0.9263 
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.5625 - loss: 0.9861
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8917
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6328 - loss: 0.8876
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6641 - loss: 0.7712
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.5703 - loss: 0.7620
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6641 - loss: 0.7051
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Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.7470
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6745 - loss: 0.7327 
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Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6875 - loss: 0.8373
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6844 - loss: 0.7355 
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Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 0.6829
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6823 - loss: 0.7194 
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Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7188 - loss: 0.7178
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7014 - loss: 0.6850 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6995 - loss: 0.6869 - val_accuracy: 0.7209 - val_loss: 0.6325
Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7109 - loss: 0.6677
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7031 - loss: 0.6804 
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Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6953 - loss: 0.7590
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7047 - loss: 0.6923 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7030 - loss: 0.6900 - val_accuracy: 0.7163 - val_loss: 0.6444
Epoch 17/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6797 - loss: 0.6214
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7051 - loss: 0.6732 
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Epoch 18/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7109 - loss: 0.5667
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7266 - loss: 0.6605 
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Epoch 19/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7188 - loss: 0.5884
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7118 - loss: 0.6610 
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Epoch 20/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7812 - loss: 0.5715
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7248 - loss: 0.6497 
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Epoch 21/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6953 - loss: 0.6855
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7119 - loss: 0.6662 
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Epoch 22/108

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Epoch 23/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7272 - loss: 0.6340 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7242 - loss: 0.6453 - val_accuracy: 0.7251 - val_loss: 0.6231
Epoch 24/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7354 - loss: 0.6364 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 360ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 73.61 [%]
F1-score capturado en la ejecución 18: 74.66 [%]

=== 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}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:22[0m 851ms/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 774us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 921us/step
Global accuracy score (validation) = 69.66 [%]
Global F1 score (validation) = 70.77 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[4.2800808e-01 5.6845623e-01 8.5236516e-04 2.6834460e-03]
 [5.9940076e-01 3.7752157e-01 1.4003450e-02 9.0741627e-03]
 [4.7498420e-01 5.1701695e-01 2.6030238e-03 5.3958213e-03]
 ...
 [2.5234204e-03 5.0396863e-03 5.4340912e-03 9.8700279e-01]
 [7.5423543e-04 2.3477755e-03 9.7044016e-04 9.9592751e-01]
 [1.9001488e-02 1.1118121e-02 9.4804943e-01 2.1830963e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 77.54 [%]
Global accuracy score (test) = 73.39 [%]
Global F1 score (train) = 77.92 [%]
Global F1 score (test) = 74.42 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.55      0.59      0.57       350
MODERATE-INTENSITY       0.55      0.57      0.56       350
         SEDENTARY       0.96      0.96      0.96       350
VIGOROUS-INTENSITY       0.96      0.82      0.89       299

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

2025-11-05 10:26:32.034881: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:26:32.046080: 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 00:00:1762334792.059125 2905344 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334792.063067 2905344 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334792.073074 2905344 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334792.073092 2905344 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334792.073093 2905344 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334792.073094 2905344 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:26:32.076243: 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 00:00:1762334794.294836 2905344 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334795.682886 2905452 service.cc:152] XLA service 0x71cd0c009f80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334795.682921 2905452 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:26:35.722546: 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 00:00:1762334795.839934 2905452 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334798.277724 2905452 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 2/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1768 
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Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5078 - loss: 1.0483
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Epoch 4/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6011 - loss: 0.9274 
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Epoch 5/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6084 - loss: 0.8914 
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Epoch 6/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6429 - loss: 0.8226 
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Epoch 7/108

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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7578 - loss: 0.6846
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6678 - loss: 0.7633 
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8516
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6724 - loss: 0.7556 
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Epoch 10/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6851 - loss: 0.7427 
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Epoch 11/108

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Epoch 12/108

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[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6924 - loss: 0.7159 
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Epoch 13/108

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Epoch 14/108

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Epoch 15/108

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Epoch 16/108

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[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6946 - loss: 0.6966 - val_accuracy: 0.7072 - val_loss: 0.6298

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 369ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 19: 73.39 [%]
F1-score capturado en la ejecución 19: 74.42 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:28[0m 868ms/step
[1m 71/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 725us/step  
[1m145/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 704us/step
[1m222/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 686us/step
[1m297/310[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 682us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m69/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 736us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 864us/step
Global accuracy score (validation) = 71.21 [%]
Global F1 score (validation) = 71.99 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.0839096e-01 4.8654544e-01 1.1244856e-03 3.9391238e-03]
 [4.8136893e-01 5.1367038e-01 6.0415559e-04 4.3565757e-03]
 [5.9639823e-01 3.9226115e-01 5.2851862e-03 6.0554892e-03]
 ...
 [1.5668037e-03 3.6728426e-03 1.5693745e-03 9.9319094e-01]
 [1.9890512e-03 4.4619944e-03 3.1433448e-03 9.9040556e-01]
 [1.4855163e-02 1.1001134e-02 9.3552494e-01 3.8618799e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.27 [%]
Global accuracy score (test) = 72.65 [%]
Global F1 score (train) = 75.5 [%]
Global F1 score (test) = 73.47 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.52      0.68      0.59       350
MODERATE-INTENSITY       0.55      0.44      0.49       350
         SEDENTARY       0.96      0.95      0.96       350
VIGOROUS-INTENSITY       0.97      0.85      0.90       299

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

2025-11-05 10:26:55.115998: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:26:55.127422: 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 00:00:1762334815.140888 2907718 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334815.145089 2907718 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334815.154858 2907718 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334815.154876 2907718 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334815.154877 2907718 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334815.154879 2907718 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:26:55.158003: 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 00:00:1762334817.407233 2907718 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334818.789574 2907850 service.cc:152] XLA service 0x7cd1e000b090 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334818.789616 2907850 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:26:58.825521: 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 00:00:1762334818.942431 2907850 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334821.351517 2907850 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:15[0m 3s/step - accuracy: 0.2109 - loss: 2.8759
[1m36/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3317 - loss: 2.1367 
[1m76/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3578 - loss: 1.9304
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.3588 - loss: 1.9226
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 52ms/step - accuracy: 0.3593 - loss: 1.9189 - val_accuracy: 0.5741 - val_loss: 0.8931
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5234 - loss: 1.1319
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1617 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4894 - loss: 1.1542 - val_accuracy: 0.6218 - val_loss: 0.8363
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5078 - loss: 1.1027
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0450 
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5859 - loss: 0.8397
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5803 - loss: 0.9382 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5874 - loss: 0.9332 - val_accuracy: 0.6612 - val_loss: 0.7116
Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8855
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6329 - loss: 0.8513 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6307 - loss: 0.8556 - val_accuracy: 0.6678 - val_loss: 0.6947
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6016 - loss: 0.7988
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6294 - loss: 0.8328 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6338 - loss: 0.8317 - val_accuracy: 0.6910 - val_loss: 0.6546
Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6406 - loss: 0.9663
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6638 - loss: 0.8074 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6598 - loss: 0.8039 - val_accuracy: 0.6956 - val_loss: 0.6581
Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8817
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6541 - loss: 0.8046 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6589 - loss: 0.7918 - val_accuracy: 0.7166 - val_loss: 0.6353
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5938 - loss: 0.8828
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6713 - loss: 0.7618 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6736 - loss: 0.7570 - val_accuracy: 0.7079 - val_loss: 0.6161
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6641 - loss: 0.8325
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6741 - loss: 0.7230 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6748 - loss: 0.7307 - val_accuracy: 0.7145 - val_loss: 0.6200
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 0.7239
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6788 - loss: 0.7177 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6813 - loss: 0.7263 - val_accuracy: 0.7103 - val_loss: 0.6468
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7109 - loss: 0.6309
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7046 - loss: 0.6804 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.6976 - loss: 0.6992
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6975 - loss: 0.6994 - val_accuracy: 0.7131 - val_loss: 0.6283
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.7188 - loss: 0.6553
[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6902 - loss: 0.7095 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6904 - loss: 0.7119 - val_accuracy: 0.7282 - val_loss: 0.6257
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.6953 - loss: 0.6298
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6811 - loss: 0.6900 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6893 - loss: 0.6891 - val_accuracy: 0.7212 - val_loss: 0.6327

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 357ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 72.65 [%]
F1-score capturado en la ejecución 20: 73.47 [%]

=== 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}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:33[0m 885ms/step
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[1m288/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 701us/step
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[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m68/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 747us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 872us/step
Global accuracy score (validation) = 71.59 [%]
Global F1 score (validation) = 72.36 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.57734174 0.40609837 0.007458   0.00910189]
 [0.5436179  0.44394332 0.00395075 0.00848807]
 [0.49470106 0.49430856 0.00309047 0.00789988]
 ...
 [0.00461062 0.00832713 0.00735956 0.9797028 ]
 [0.00505829 0.00853049 0.01474752 0.9716637 ]
 [0.02126706 0.01641278 0.9304447  0.03187539]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.91 [%]
Global accuracy score (test) = 71.39 [%]
Global F1 score (train) = 75.28 [%]
Global F1 score (test) = 72.5 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.56      0.53       350
MODERATE-INTENSITY       0.51      0.51      0.51       350
         SEDENTARY       0.96      0.97      0.96       350
VIGOROUS-INTENSITY       0.96      0.84      0.90       299

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

2025-11-05 10:27:17.671267: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:27:17.682674: 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 00:00:1762334837.696008 2909939 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334837.700007 2909939 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334837.710028 2909939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334837.710045 2909939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334837.710053 2909939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334837.710054 2909939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:27:17.713167: 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 00:00:1762334839.933081 2909939 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334841.271000 2910046 service.cc:152] XLA service 0x73098c00a520 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334841.271025 2910046 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:27:21.304325: 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 00:00:1762334841.420674 2910046 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334843.839899 2910046 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:13[0m 3s/step - accuracy: 0.2188 - loss: 2.6968
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3344 - loss: 2.0945 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.3604 - loss: 1.8950
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 52ms/step - accuracy: 0.3609 - loss: 1.8912 - val_accuracy: 0.5713 - val_loss: 0.9026
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.5469 - loss: 1.0703
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1982 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4923 - loss: 1.1721 - val_accuracy: 0.6197 - val_loss: 0.8164
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0911
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5316 - loss: 1.0229 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5419 - loss: 1.0124 - val_accuracy: 0.6552 - val_loss: 0.7623
Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6328 - loss: 0.8927
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5834 - loss: 0.9431 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5889 - loss: 0.9355 - val_accuracy: 0.6763 - val_loss: 0.7002
Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5859 - loss: 0.7755
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6024 - loss: 0.8684 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6063 - loss: 0.8698 - val_accuracy: 0.6745 - val_loss: 0.6894
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6484 - loss: 0.7358
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6372 - loss: 0.8161 
[1m75/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6382 - loss: 0.8188
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6384 - loss: 0.8187 - val_accuracy: 0.6928 - val_loss: 0.6699
Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6094 - loss: 0.7884
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6428 - loss: 0.8074 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6456 - loss: 0.7991 - val_accuracy: 0.6864 - val_loss: 0.6651
Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5625 - loss: 0.8327
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6379 - loss: 0.8015 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6476 - loss: 0.7863 - val_accuracy: 0.6980 - val_loss: 0.6371
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8238
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6817 - loss: 0.7552 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6816 - loss: 0.7495 - val_accuracy: 0.7019 - val_loss: 0.6364
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7109 - loss: 0.7831
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6843 - loss: 0.7446 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6826 - loss: 0.7391 - val_accuracy: 0.7107 - val_loss: 0.6263
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6328 - loss: 0.8933
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6640 - loss: 0.7608 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.6700 - loss: 0.7477
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6702 - loss: 0.7474 - val_accuracy: 0.7216 - val_loss: 0.6102
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6016 - loss: 0.8867
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6761 - loss: 0.7486 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6802 - loss: 0.7363 - val_accuracy: 0.7110 - val_loss: 0.6149
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6562 - loss: 0.7439
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6817 - loss: 0.7146 
[1m76/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6838 - loss: 0.7105
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6838 - loss: 0.7103 - val_accuracy: 0.7096 - val_loss: 0.6290
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.7344 - loss: 0.6031
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6889 - loss: 0.7001 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6890 - loss: 0.7007 - val_accuracy: 0.7198 - val_loss: 0.6253
Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 0.7365
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6721 - loss: 0.7117 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6775 - loss: 0.7042 - val_accuracy: 0.7065 - val_loss: 0.6379
Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8323
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6894 - loss: 0.7112 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6945 - loss: 0.7030 - val_accuracy: 0.7131 - val_loss: 0.6438

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 382ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 21: 71.39 [%]
F1-score capturado en la ejecución 21: 72.5 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:22[0m 849ms/step
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[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[1m72/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 709us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 821us/step
Global accuracy score (validation) = 71.24 [%]
Global F1 score (validation) = 70.88 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.61386395 0.37036744 0.00921207 0.00655657]
 [0.5490667  0.43605348 0.00583664 0.00904315]
 [0.53262484 0.45800036 0.00258959 0.00678525]
 ...
 [0.00283416 0.00597565 0.00350275 0.9876874 ]
 [0.00308365 0.00662819 0.00220678 0.98808134]
 [0.02243572 0.02037242 0.91772896 0.03946299]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 73.78 [%]
Global accuracy score (test) = 71.46 [%]
Global F1 score (train) = 72.56 [%]
Global F1 score (test) = 71.24 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.50      0.77      0.61       350
MODERATE-INTENSITY       0.51      0.29      0.37       350
         SEDENTARY       0.96      0.97      0.97       350
VIGOROUS-INTENSITY       0.97      0.84      0.90       299

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

2025-11-05 10:27:40.658456: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:27:40.670024: 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 00:00:1762334860.683710 2912307 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334860.688017 2912307 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334860.698121 2912307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334860.698139 2912307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334860.698140 2912307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334860.698141 2912307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:27:40.701308: 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 00:00:1762334862.922660 2912307 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334864.302346 2912442 service.cc:152] XLA service 0x7a0ec401b840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334864.302377 2912442 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:27:44.337764: 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 00:00:1762334864.454767 2912442 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334866.794697 2912442 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:09[0m 3s/step - accuracy: 0.3203 - loss: 2.3814
[1m36/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3365 - loss: 2.0835 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.3628 - loss: 1.8913
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 52ms/step - accuracy: 0.3633 - loss: 1.8879 - val_accuracy: 0.5660 - val_loss: 0.9093
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.5469 - loss: 1.2490
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.2245 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4818 - loss: 1.1935 - val_accuracy: 0.6011 - val_loss: 0.8275
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0935
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0379 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5409 - loss: 1.0271 - val_accuracy: 0.6236 - val_loss: 0.7627
Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9752
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5855 - loss: 0.9305 
[1m76/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5890 - loss: 0.9230
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5891 - loss: 0.9227 - val_accuracy: 0.6650 - val_loss: 0.7040
Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6641 - loss: 1.0182
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6130 - loss: 0.8955 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6123 - loss: 0.8889 - val_accuracy: 0.6664 - val_loss: 0.6823
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8774
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6486 - loss: 0.8388 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6434 - loss: 0.8356 - val_accuracy: 0.6843 - val_loss: 0.6547
Epoch 7/108

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Epoch 8/108

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[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6503 - loss: 0.7967 
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Epoch 9/108

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Epoch 10/108

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Epoch 11/108

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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6908 - loss: 0.7071 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 379ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 22: 71.46 [%]
F1-score capturado en la ejecución 22: 71.24 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:26[0m 861ms/step
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[1m67/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 761us/step
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Global accuracy score (validation) = 67.52 [%]
Global F1 score (validation) = 64.76 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.5904803  0.40304035 0.00193902 0.00454027]
 [0.60221195 0.3506543  0.03314805 0.01398564]
 [0.54373115 0.445528   0.00158021 0.00916071]
 ...
 [0.00644762 0.01170242 0.02630383 0.9555462 ]
 [0.00703168 0.01281906 0.0295521  0.9505971 ]
 [0.01200961 0.01098031 0.9529446  0.02406552]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 71.02 [%]
Global accuracy score (test) = 72.05 [%]
Global F1 score (train) = 68.02 [%]
Global F1 score (test) = 70.53 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.87      0.64       350
MODERATE-INTENSITY       0.56      0.22      0.32       350
         SEDENTARY       0.96      0.97      0.96       350
VIGOROUS-INTENSITY       0.96      0.84      0.89       299

          accuracy                           0.72      1349
         macro avg       0.75      0.72      0.71      1349
      weighted avg       0.74      0.72      0.70      1349

2025-11-05 10:28:02.655275: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:28:02.666654: 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 00:00:1762334882.679800 2914235 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334882.683886 2914235 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334882.693751 2914235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334882.693768 2914235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334882.693770 2914235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334882.693771 2914235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:28:02.696889: 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 00:00:1762334884.959893 2914235 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334886.351596 2914367 service.cc:152] XLA service 0x74c788009840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334886.351642 2914367 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:28:06.394165: 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 00:00:1762334886.515533 2914367 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334888.942754 2914367 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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[1m32/78[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3355 - loss: 2.1717 
[1m75/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.9453
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[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 52ms/step - accuracy: 0.3627 - loss: 1.9299 - val_accuracy: 0.5815 - val_loss: 0.8924
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2965
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.2215 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4801 - loss: 1.1929 - val_accuracy: 0.6197 - val_loss: 0.8251
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1487
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0581 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5311 - loss: 1.0399 - val_accuracy: 0.6447 - val_loss: 0.7882
Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.5859 - loss: 0.9777
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5730 - loss: 0.9568 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5782 - loss: 0.9494 - val_accuracy: 0.6766 - val_loss: 0.7190
Epoch 5/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6086 - loss: 0.8935 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6119 - loss: 0.8857 - val_accuracy: 0.6724 - val_loss: 0.7010
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6094 - loss: 0.7751
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6165 - loss: 0.8467 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6231 - loss: 0.8452 - val_accuracy: 0.6843 - val_loss: 0.6819
Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7998
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6362 - loss: 0.8135 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6395 - loss: 0.8115 - val_accuracy: 0.7068 - val_loss: 0.6571
Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6250 - loss: 0.7495
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6456 - loss: 0.7826 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6511 - loss: 0.7804 - val_accuracy: 0.6787 - val_loss: 0.6683
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6953 - loss: 0.6777
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6516 - loss: 0.7485 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6566 - loss: 0.7525 - val_accuracy: 0.6973 - val_loss: 0.6461
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.7188 - loss: 0.5999
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6806 - loss: 0.7216 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6784 - loss: 0.7308 - val_accuracy: 0.6861 - val_loss: 0.6640
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6953 - loss: 0.7859
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6811 - loss: 0.7419 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6790 - loss: 0.7338 - val_accuracy: 0.7117 - val_loss: 0.6312
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6953 - loss: 0.6519
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6804 - loss: 0.7136 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6805 - loss: 0.7141 - val_accuracy: 0.7128 - val_loss: 0.6215
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6250 - loss: 0.7609
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6926 - loss: 0.7007 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6937 - loss: 0.7004 - val_accuracy: 0.7005 - val_loss: 0.6348
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.7500 - loss: 0.6069
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6938 - loss: 0.6945 
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Epoch 15/108

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Epoch 16/108

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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7076 - loss: 0.6811 
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Epoch 17/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6972 - loss: 0.6777 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 356ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 23: 72.05 [%]
F1-score capturado en la ejecución 23: 70.53 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:33[0m 884ms/step
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[1m142/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 715us/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m73/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 695us/step
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Global accuracy score (validation) = 71.31 [%]
Global F1 score (validation) = 71.44 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.6201692  0.3708155  0.00597139 0.0030439 ]
 [0.61586976 0.3733924  0.00712168 0.00361608]
 [0.41081753 0.5542737  0.00324858 0.03166026]
 ...
 [0.00185952 0.00397503 0.00238679 0.9917787 ]
 [0.00205394 0.00438102 0.00285787 0.9907072 ]
 [0.02097146 0.01539464 0.9347119  0.02892206]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.12 [%]
Global accuracy score (test) = 72.35 [%]
Global F1 score (train) = 74.96 [%]
Global F1 score (test) = 72.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.52      0.70      0.59       350
MODERATE-INTENSITY       0.52      0.39      0.44       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.96      0.86      0.91       299

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

2025-11-05 10:28:25.920933: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:28:25.932309: 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 00:00:1762334905.946268 2916718 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334905.950230 2916718 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334905.960150 2916718 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334905.960169 2916718 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334905.960177 2916718 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334905.960179 2916718 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:28:25.963290: 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 00:00:1762334908.238037 2916718 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334909.622020 2916857 service.cc:152] XLA service 0x7bbadc004fd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334909.622048 2916857 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:28:29.656824: 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 00:00:1762334909.779791 2916857 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334912.169344 2916857 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:14[0m 3s/step - accuracy: 0.2031 - loss: 2.7621
[1m35/78[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3386 - loss: 2.1281 
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Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4609 - loss: 1.2825
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.2589 
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Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9457
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5427 - loss: 1.0271 
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5391 - loss: 1.0141
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5832 - loss: 0.9379 
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5703 - loss: 0.9960
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6234 - loss: 0.8689 
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6719 - loss: 0.8403
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6429 - loss: 0.8259 
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Epoch 7/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6510 - loss: 0.7864 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6539 - loss: 0.7836
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9410
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6480 - loss: 0.7915 
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7031 - loss: 0.8007
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6716 - loss: 0.7495 
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6797 - loss: 0.9832
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6584 - loss: 0.7637 
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Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6719 - loss: 0.7181
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6695 - loss: 0.7343 
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Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.7641
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6807 - loss: 0.7237 
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Epoch 13/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6865 - loss: 0.7074 
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Epoch 14/108

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Epoch 15/108

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Epoch 16/108

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Epoch 17/108

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Epoch 18/108

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[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7091 - loss: 0.6560 
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Epoch 19/108

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Epoch 20/108

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Epoch 21/108

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[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7107 - loss: 0.6388 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 380ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 24: 72.35 [%]
F1-score capturado en la ejecución 24: 72.95 [%]

=== 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}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:28[0m 869ms/step
[1m 72/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 712us/step  
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m71/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 714us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 831us/step
Global accuracy score (validation) = 71.95 [%]
Global F1 score (validation) = 72.32 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.76214194e-01 4.18304235e-01 2.65898905e-03 2.82255723e-03]
 [6.54852509e-01 3.34531814e-01 6.71006134e-03 3.90566606e-03]
 [6.54133022e-01 3.35116535e-01 6.78126467e-03 3.96926934e-03]
 ...
 [5.79510233e-04 1.54538953e-03 1.15751172e-03 9.96717632e-01]
 [1.06542720e-03 2.53735040e-03 2.65218969e-03 9.93744969e-01]
 [1.12857055e-02 8.52356292e-03 9.45961297e-01 3.42293680e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 76.38 [%]
Global accuracy score (test) = 75.39 [%]
Global F1 score (train) = 76.23 [%]
Global F1 score (test) = 75.58 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.57      0.74      0.64       350
MODERATE-INTENSITY       0.60      0.44      0.51       350
         SEDENTARY       0.98      0.97      0.98       350
VIGOROUS-INTENSITY       0.91      0.88      0.90       299

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

2025-11-05 10:28:50.085031: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:28:50.096698: 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 00:00:1762334930.110538 2919563 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334930.114804 2919563 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334930.124557 2919563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334930.124574 2919563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334930.124581 2919563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334930.124582 2919563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:28:50.127702: 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 00:00:1762334932.404215 2919563 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334933.809460 2919678 service.cc:152] XLA service 0x7a84c400a690 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334933.809505 2919678 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:28:53.846032: 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 00:00:1762334933.969071 2919678 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334936.423560 2919678 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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[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3296 - loss: 2.1255 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.9095
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Epoch 2/108

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[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1885 
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Epoch 3/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0088 
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Epoch 4/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5943 - loss: 0.9348 
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Epoch 5/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6356 - loss: 0.8421 
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[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6317 - loss: 0.8463 - val_accuracy: 0.6833 - val_loss: 0.6902
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6875 - loss: 0.8150
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6566 - loss: 0.8016 
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Epoch 7/108

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[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6692 - loss: 0.7743 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6647 - loss: 0.7774 - val_accuracy: 0.6857 - val_loss: 0.6714
Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6797 - loss: 0.6339
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6505 - loss: 0.7703 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6555 - loss: 0.7670 - val_accuracy: 0.6850 - val_loss: 0.6627
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6562 - loss: 0.7600
[1m46/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6532 - loss: 0.7674 
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Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7422 - loss: 0.6720
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6770 - loss: 0.7351 
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Epoch 11/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6614 - loss: 0.7597 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6686 - loss: 0.7440 - val_accuracy: 0.6994 - val_loss: 0.6236
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.7266 - loss: 0.6533
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6881 - loss: 0.7122 
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Epoch 13/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7026 - loss: 0.6996 
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Epoch 14/108

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Epoch 15/108

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Epoch 16/108

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Epoch 17/108

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Epoch 18/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7032 - loss: 0.6724 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 370ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 25: 75.39 [%]
F1-score capturado en la ejecución 25: 75.58 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:30[0m 874ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m75/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 683us/step
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Global accuracy score (validation) = 70.68 [%]
Global F1 score (validation) = 71.53 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[4.70197320e-01 5.27685642e-01 6.36826677e-04 1.48022722e-03]
 [3.64931822e-01 6.17313743e-01 1.48837874e-03 1.62661131e-02]
 [5.65912127e-01 4.18083012e-01 8.75194557e-03 7.25295115e-03]
 ...
 [3.11089098e-03 5.15621621e-03 8.84995051e-03 9.82882977e-01]
 [4.57448699e-03 7.16328714e-03 1.39367823e-02 9.74325478e-01]
 [1.41387135e-02 9.25447792e-03 9.37464714e-01 3.91420610e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.23 [%]
Global accuracy score (test) = 74.05 [%]
Global F1 score (train) = 75.45 [%]
Global F1 score (test) = 74.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.56      0.51      0.53       350
MODERATE-INTENSITY       0.54      0.63      0.58       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.94      0.88      0.91       299

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

2025-11-05 10:29:13.560050: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:29:13.571361: 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 00:00:1762334953.584354 2922119 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334953.588488 2922119 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334953.598331 2922119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334953.598348 2922119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334953.598349 2922119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334953.598351 2922119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:29:13.601569: 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 00:00:1762334955.800853 2922119 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334957.163608 2922247 service.cc:152] XLA service 0x70e82000ab90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334957.163644 2922247 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:29:17.199622: 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 00:00:1762334957.320928 2922247 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334959.681808 2922247 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 2/108

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Epoch 3/108

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Epoch 4/108

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Epoch 5/108

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Epoch 6/108

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Epoch 7/108

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[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6353 - loss: 0.8193 
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Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.6641 - loss: 0.8746
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6431 - loss: 0.8107 
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Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6953 - loss: 0.8566
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6663 - loss: 0.7709 
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Epoch 10/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6810 - loss: 0.7193 
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Epoch 11/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6811 - loss: 0.7242 
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Epoch 12/108

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[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6689 - loss: 0.7538 
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Epoch 13/108

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Epoch 14/108

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[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6898 - loss: 0.7038 - val_accuracy: 0.7124 - val_loss: 0.6502
Epoch 15/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7074 - loss: 0.6810 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 360ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 26: 74.05 [%]
F1-score capturado en la ejecución 26: 74.95 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:33[0m 885ms/step
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[1m283/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 718us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 759us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 858us/step
Global accuracy score (validation) = 71.59 [%]
Global F1 score (validation) = 72.6 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.5212265e-01 4.2393050e-01 1.2815857e-02 1.1131029e-02]
 [4.4309205e-01 5.4981869e-01 1.2414217e-03 5.8477959e-03]
 [5.1583475e-01 4.7413981e-01 3.6680875e-03 6.3573313e-03]
 ...
 [2.1545966e-03 3.8941198e-03 9.8390284e-04 9.9296737e-01]
 [5.9585124e-03 8.8923248e-03 4.5551006e-03 9.8059410e-01]
 [2.2602608e-02 1.6731100e-02 9.0032494e-01 6.0341313e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.35 [%]
Global accuracy score (test) = 71.31 [%]
Global F1 score (train) = 74.76 [%]
Global F1 score (test) = 72.37 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.47      0.49       350
MODERATE-INTENSITY       0.50      0.58      0.54       350
         SEDENTARY       0.98      0.95      0.96       350
VIGOROUS-INTENSITY       0.93      0.88      0.91       299

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

2025-11-05 10:29:36.353371: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:29:36.365201: 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 00:00:1762334976.378719 2924433 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334976.382954 2924433 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334976.393030 2924433 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334976.393050 2924433 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334976.393051 2924433 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334976.393053 2924433 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:29:36.396316: 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 00:00:1762334978.635911 2924433 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762334980.014029 2924562 service.cc:152] XLA service 0x7558cc00a880 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762334980.014074 2924562 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:29:40.049349: 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 00:00:1762334980.166330 2924562 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762334982.604006 2924562 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 2/108

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Epoch 3/108

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Epoch 4/108

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Epoch 5/108

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Epoch 6/108

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Epoch 7/108

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Epoch 8/108

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Epoch 9/108

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Epoch 10/108

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Epoch 11/108

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Epoch 12/108

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[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6868 - loss: 0.7089 
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Epoch 13/108

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[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6872 - loss: 0.7140 
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Epoch 14/108

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Epoch 15/108

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Saved model to disk.

Accuracy capturado en la ejecución 27: 71.31 [%]
F1-score capturado en la ejecución 27: 72.37 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:23[0m 851ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m69/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 741us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 845us/step
Global accuracy score (validation) = 70.82 [%]
Global F1 score (validation) = 71.8 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.57546675 0.40711728 0.00945684 0.00795905]
 [0.5570404  0.42783794 0.00691586 0.00820586]
 [0.39584506 0.58645934 0.00176071 0.01593488]
 ...
 [0.00236273 0.00487431 0.00277    0.9899929 ]
 [0.00174404 0.00362325 0.00256683 0.99206597]
 [0.02671706 0.02006248 0.8992205  0.05399995]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.82 [%]
Global accuracy score (test) = 73.76 [%]
Global F1 score (train) = 74.96 [%]
Global F1 score (test) = 74.74 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.56      0.52      0.54       350
MODERATE-INTENSITY       0.53      0.61      0.57       350
         SEDENTARY       0.98      0.97      0.98       350
VIGOROUS-INTENSITY       0.96      0.87      0.91       299

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

2025-11-05 10:29:59.224459: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:29:59.235707: 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 00:00:1762334999.248960 2926717 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762334999.253063 2926717 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762334999.262948 2926717 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334999.262974 2926717 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334999.262975 2926717 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762334999.262976 2926717 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:29:59.265946: 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 00:00:1762335001.490847 2926717 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335002.877094 2926848 service.cc:152] XLA service 0x70e60c005bb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335002.877130 2926848 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:30:02.914481: 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 00:00:1762335003.037267 2926848 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335005.427784 2926848 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:14[0m 3s/step - accuracy: 0.2578 - loss: 2.6504
[1m36/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 2.0476 
[1m74/78[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3686 - loss: 1.8650
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.3704 - loss: 1.8508
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 52ms/step - accuracy: 0.3708 - loss: 1.8474 - val_accuracy: 0.5674 - val_loss: 0.9098
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4609 - loss: 1.2051
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1974 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1740
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1731 - val_accuracy: 0.6096 - val_loss: 0.8371
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1250
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0313 
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Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9855
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5718 - loss: 0.9506 
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Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8036
[1m38/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6164 - loss: 0.8718 
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Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6875 - loss: 0.6481
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6358 - loss: 0.8120 
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Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6641 - loss: 0.7266
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6487 - loss: 0.8084 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.6488 - loss: 0.8062
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6489 - loss: 0.8060 - val_accuracy: 0.6724 - val_loss: 0.6613
Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7422 - loss: 0.6701
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6658 - loss: 0.7736 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6666 - loss: 0.7683 - val_accuracy: 0.6749 - val_loss: 0.6839
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6016 - loss: 0.8946
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6567 - loss: 0.7793 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6613 - loss: 0.7690 - val_accuracy: 0.6928 - val_loss: 0.6385
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6328 - loss: 0.8233
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6692 - loss: 0.7647 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6719 - loss: 0.7525 - val_accuracy: 0.6942 - val_loss: 0.6280
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6797 - loss: 0.7543
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6760 - loss: 0.7347 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6760 - loss: 0.7336 - val_accuracy: 0.6875 - val_loss: 0.6483
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8074
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6740 - loss: 0.7461 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.6782 - loss: 0.7344
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6783 - loss: 0.7342 - val_accuracy: 0.6903 - val_loss: 0.6303
Epoch 13/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6923 - loss: 0.7009 
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Epoch 14/108

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[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6755 - loss: 0.7113 
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Epoch 15/108

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[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6882 - loss: 0.6936 
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 364ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 73.76 [%]
F1-score capturado en la ejecución 28: 74.74 [%]

=== 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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:31[0m 880ms/step
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[1m291/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 695us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m74/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 689us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 813us/step
Global accuracy score (validation) = 70.54 [%]
Global F1 score (validation) = 70.1 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.88697910e-01 3.85544747e-01 1.41688315e-02 1.15884794e-02]
 [5.90230286e-01 3.86078209e-01 1.27732344e-02 1.09182252e-02]
 [5.78614175e-01 4.02242482e-01 9.27866343e-03 9.86468792e-03]
 ...
 [4.12681792e-03 6.54122280e-03 6.19223388e-03 9.83139694e-01]
 [8.96621030e-04 1.61827251e-03 1.07309443e-03 9.96411920e-01]
 [1.56832468e-02 1.13312798e-02 9.43527102e-01 2.94583682e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 74.32 [%]
Global accuracy score (test) = 72.13 [%]
Global F1 score (train) = 73.22 [%]
Global F1 score (test) = 72.18 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.77      0.61       350
MODERATE-INTENSITY       0.53      0.32      0.40       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.96      0.86      0.91       299

          accuracy                           0.72      1349
         macro avg       0.75      0.73      0.72      1349
      weighted avg       0.74      0.72      0.71      1349

2025-11-05 10:30:22.040926: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 10:30:22.052807: 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 00:00:1762335022.066267 2929030 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335022.070378 2929030 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335022.080479 2929030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335022.080499 2929030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335022.080501 2929030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335022.080502 2929030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:30:22.083594: 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 00:00:1762335024.274704 2929030 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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 1/108
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335025.621368 2929148 service.cc:152] XLA service 0x7ce7b0004210 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335025.621396 2929148 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:30:25.657286: 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 00:00:1762335025.780876 2929148 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335028.204197 2929148 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:15[0m 3s/step - accuracy: 0.2109 - loss: 2.6616
[1m37/78[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3156 - loss: 2.0519 
[1m77/78[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.8656
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 30ms/step - accuracy: 0.3462 - loss: 1.8620
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 52ms/step - accuracy: 0.3468 - loss: 1.8584 - val_accuracy: 0.5495 - val_loss: 0.9079
Epoch 2/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.4531 - loss: 1.2558
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4703 - loss: 1.2215 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4790 - loss: 1.1872 - val_accuracy: 0.6225 - val_loss: 0.8036
Epoch 3/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.5469 - loss: 1.0726
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0266 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5488 - loss: 1.0173 - val_accuracy: 0.6573 - val_loss: 0.7245
Epoch 4/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.5938 - loss: 1.0945
[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5830 - loss: 0.9474 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5890 - loss: 0.9326 - val_accuracy: 0.6766 - val_loss: 0.6851
Epoch 5/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.6172 - loss: 0.8829
[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6101 - loss: 0.8816 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6129 - loss: 0.8725 - val_accuracy: 0.6875 - val_loss: 0.6526
Epoch 6/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.6250 - loss: 0.8878
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6456 - loss: 0.8170 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6448 - loss: 0.8160 - val_accuracy: 0.6871 - val_loss: 0.6671
Epoch 7/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.6328 - loss: 0.9037
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6647 - loss: 0.7835 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6614 - loss: 0.7821 - val_accuracy: 0.6949 - val_loss: 0.6475
Epoch 8/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6875 - loss: 0.7923
[1m39/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6554 - loss: 0.7727 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6581 - loss: 0.7684 - val_accuracy: 0.6896 - val_loss: 0.6494
Epoch 9/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8544
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6621 - loss: 0.7645 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6649 - loss: 0.7612 - val_accuracy: 0.7086 - val_loss: 0.6254
Epoch 10/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.6016 - loss: 0.8377
[1m42/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6610 - loss: 0.7432 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6641 - loss: 0.7407 - val_accuracy: 0.7012 - val_loss: 0.6446
Epoch 11/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7031 - loss: 0.7111
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6765 - loss: 0.7045 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6781 - loss: 0.7132 - val_accuracy: 0.7152 - val_loss: 0.6076
Epoch 12/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6562 - loss: 0.7842
[1m45/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6908 - loss: 0.7095 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6868 - loss: 0.7128 - val_accuracy: 0.7131 - val_loss: 0.6216
Epoch 13/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7031 - loss: 0.6940
[1m40/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7046 - loss: 0.7021 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.7023 - loss: 0.7032 - val_accuracy: 0.7138 - val_loss: 0.6293
Epoch 14/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.6953 - loss: 0.7615
[1m43/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6781 - loss: 0.7333 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6841 - loss: 0.7185 - val_accuracy: 0.7093 - val_loss: 0.6252
Epoch 15/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.7031 - loss: 0.7085
[1m44/78[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6868 - loss: 0.6880 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6879 - loss: 0.6903 - val_accuracy: 0.7142 - val_loss: 0.6085
Epoch 16/108

[1m 1/78[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.6719 - loss: 0.7125
[1m41/78[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6857 - loss: 0.7051 
[1m78/78[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.6874 - loss: 0.7032 - val_accuracy: 0.7173 - val_loss: 0.6377

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 388ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 72.13 [%]
F1-score capturado en la ejecución 29: 72.18 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (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, 3, 64)          │        48,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 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.
(1349, 3, 250)
(9904, 3, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4:25[0m 858ms/step
[1m 66/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 771us/step  
[1m140/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 723us/step
[1m212/310[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 714us/step
[1m288/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 700us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 851us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 913us/step
Global accuracy score (validation) = 72.61 [%]
Global F1 score (validation) = 73.69 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[0.5577644  0.42724976 0.00892677 0.00605902]
 [0.5599006  0.42168632 0.01099501 0.00741819]
 [0.5366908  0.42862415 0.02193847 0.01274654]
 ...
 [0.00294543 0.00761211 0.00246421 0.9869782 ]
 [0.00175571 0.00468916 0.00169808 0.9918572 ]
 [0.05194491 0.05384568 0.69217366 0.20203578]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 75.69 [%]
Global accuracy score (test) = 74.87 [%]
Global F1 score (train) = 75.95 [%]
Global F1 score (test) = 75.87 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.56      0.65      0.60       350
MODERATE-INTENSITY       0.56      0.53      0.54       350
         SEDENTARY       0.98      0.97      0.98       350
VIGOROUS-INTENSITY       0.98      0.86      0.91       299

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


Accuracy capturado en la ejecución 30: 74.87 [%]
F1-score capturado en la ejecución 30: 75.87 [%]

=== RESUMEN FINAL ===
Accuracies: [70.42, 71.02, 72.28, 73.83, 74.72, 72.65, 72.28, 74.57, 71.91, 72.79, 72.87, 71.76, 73.09, 73.09, 75.83, 72.28, 74.28, 73.61, 73.39, 72.65, 71.39, 71.46, 72.05, 72.35, 75.39, 74.05, 71.31, 73.76, 72.13, 74.87]
F1-scores: [71.57, 70.56, 72.9, 74.53, 75.1, 73.72, 73.42, 75.49, 72.98, 73.82, 73.82, 73.05, 74.01, 74.12, 76.02, 70.31, 74.3, 74.66, 74.42, 73.47, 72.5, 71.24, 70.53, 72.95, 75.58, 74.95, 72.37, 74.74, 72.18, 75.87]
Accuracy mean: 72.9360 | std: 1.3182
F1 mean: 73.5060 | std: 1.5586

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