2025-11-05 16:16:43.127523: 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 16:16:43.139282: 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 00:00:1762355803.153314 3570196 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762355803.157530 3570196 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762355803.168258 3570196 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762355803.168281 3570196 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762355803.168283 3570196 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762355803.168285 3570196 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:16:43.171512: 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 16:16:46,167	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-05 16:16:46,889	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-05 16:16:46,964	INFO trial.py:182 -- Creating a new dirname dir_71e21_3ca4 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,967	INFO trial.py:182 -- Creating a new dirname dir_71e21_eec5 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,969	INFO trial.py:182 -- Creating a new dirname dir_71e21_aa14 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,972	INFO trial.py:182 -- Creating a new dirname dir_71e21_77db because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,974	INFO trial.py:182 -- Creating a new dirname dir_71e21_b34c because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,976	INFO trial.py:182 -- Creating a new dirname dir_71e21_d4e7 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,979	INFO trial.py:182 -- Creating a new dirname dir_71e21_3c0c because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,981	INFO trial.py:182 -- Creating a new dirname dir_71e21_90bb because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,983	INFO trial.py:182 -- Creating a new dirname dir_71e21_393b because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,986	INFO trial.py:182 -- Creating a new dirname dir_71e21_3466 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,990	INFO trial.py:182 -- Creating a new dirname dir_71e21_8fbc because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,992	INFO trial.py:182 -- Creating a new dirname dir_71e21_5956 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,995	INFO trial.py:182 -- Creating a new dirname dir_71e21_030d because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:46,998	INFO trial.py:182 -- Creating a new dirname dir_71e21_9264 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:47,001	INFO trial.py:182 -- Creating a new dirname dir_71e21_a661 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:47,004	INFO trial.py:182 -- Creating a new dirname dir_71e21_244b because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:47,008	INFO trial.py:182 -- Creating a new dirname dir_71e21_c419 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:47,012	INFO trial.py:182 -- Creating a new dirname dir_71e21_bc84 because trial dirname 'dir_71e21' already exists.
2025-11-05 16:16:47,019	INFO trial.py:182 -- Creating a new dirname dir_71e21_6342 because trial dirname 'dir_71e21' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     ESANN_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator          │
│ Scheduler                        AsyncHyperBandScheduler        │
│ Number of trials                 20                             │
╰─────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_ESANN_acc_17_classes/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-05_16-16-45_446617_3570196/artifacts/2025-11-05_16-16-46/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-05 16:16:47. Total running time: 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 │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    PENDING            4   adam            tanh                                  128                128                  5          1.79438e-05         68 │
│ trial_71e21    PENDING            4   adam            relu                                   64                 64                  5          0.00312024         137 │
│ trial_71e21    PENDING            4   rmsprop         tanh                                  128                 64                  5          0.00257087          67 │
│ trial_71e21    PENDING            3   rmsprop         tanh                                   32                 32                  5          0.000287941         75 │
│ trial_71e21    PENDING            4   rmsprop         tanh                                  128                 32                  5          0.000330218        109 │
│ trial_71e21    PENDING            3   rmsprop         tanh                                   32                 32                  3          0.00114134         130 │
│ trial_71e21    PENDING            2   adam            tanh                                   32                 64                  5          0.00169955         147 │
│ trial_71e21    PENDING            2   adam            relu                                  128                 64                  3          0.00249985         124 │
│ trial_71e21    PENDING            3   rmsprop         relu                                   32                 32                  5          0.000412915         95 │
│ trial_71e21    PENDING            2   rmsprop         relu                                   64                 64                  3          0.00159421          98 │
│ trial_71e21    PENDING            3   adam            tanh                                   32                 64                  5          0.000334848        109 │
│ trial_71e21    PENDING            3   rmsprop         tanh                                  128                 32                  5          0.000813557         72 │
│ trial_71e21    PENDING            2   rmsprop         tanh                                   32                 32                  5          0.00267766          55 │
│ trial_71e21    PENDING            2   adam            tanh                                   64                128                  3          0.00239804          79 │
│ trial_71e21    PENDING            4   rmsprop         relu                                   64                 32                  3          0.000672            95 │
│ trial_71e21    PENDING            2   rmsprop         tanh                                  128                 32                  3          0.000317831         96 │
│ trial_71e21    PENDING            2   adam            tanh                                   64                 32                  5          2.55186e-05        127 │
│ trial_71e21    PENDING            2   adam            tanh                                  128                128                  5          8.93848e-05         65 │
│ trial_71e21    PENDING            4   adam            relu                                  128                 64                  3          4.42373e-05         52 │
│ trial_71e21    PENDING            3   rmsprop         tanh                                   32                128                  3          0.000391262        148 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            96 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00032 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            75 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00029 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            98 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00159 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           127 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           148 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00039 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            67 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00257 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           137 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00312 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           109 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00033 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            95 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00067 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            68 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            55 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00268 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
[36m(train_cnn_ray_tune pid=3571845)[0m 2025-11-05 16:16:50.164564: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
[36m(train_cnn_ray_tune pid=3571845)[0m 2025-11-05 16:16:50.185665: 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=3571837)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=3571837)[0m E0000 00:00:1762355810.266939 3573015 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=3571837)[0m E0000 00:00:1762355810.275181 3573015 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=3571837)[0m W0000 00:00:1762355810.294971 3573015 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=3571837)[0m W0000 00:00:1762355810.295011 3573015 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=3571837)[0m W0000 00:00:1762355810.295014 3573015 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=3571837)[0m W0000 00:00:1762355810.295016 3573015 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=3571837)[0m 2025-11-05 16:16:50.301097: 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=3571837)[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=3571837)[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=3571837)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=3571837)[0m 2025-11-05 16:16:53.509692: 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=3571837)[0m 2025-11-05 16:16:53.509735: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=3571837)[0m 2025-11-05 16:16:53.509743: 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=3571837)[0m 2025-11-05 16:16:53.509747: 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=3571837)[0m 2025-11-05 16:16:53.509751: 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=3571837)[0m 2025-11-05 16:16:53.509754: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=3571837)[0m 2025-11-05 16:16:53.509953: 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=3571837)[0m 2025-11-05 16:16:53.509984: 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=3571837)[0m 2025-11-05 16:16:53.509988: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           109 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00033 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           130 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00114 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            95 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00041 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            65 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            52 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_71e21 config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                          124 │
│ funcion_activacion             relu │
│ numero_filtros                   64 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                128 │
│ tasa_aprendizaje             0.0025 │
╰─────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_71e21 config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                          147 │
│ funcion_activacion             tanh │
│ numero_filtros                   64 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                 32 │
│ tasa_aprendizaje             0.0017 │
╰─────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_71e21 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            72 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00081 │
╰──────────────────────────────────────╯
Trial trial_71e21 started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_71e21 config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                           79 │
│ funcion_activacion             tanh │
│ numero_filtros                  128 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 64 │
│ tasa_aprendizaje             0.0024 │
╰─────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571837)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=3571837)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=3571837)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=3571837)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=3571837)[0m │ conv1d (Conv1D)                 │ (None, 3, 32)          │        40,032 │
[36m(train_cnn_ray_tune pid=3571837)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3571837)[0m │ layer_normalization             │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=3571837)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3571837)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3571837)[0m │ dropout (Dropout)               │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=3571837)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3571837)[0m │ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         5,152 │
[36m(train_cnn_ray_tune pid=3571837)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3571837)[0m │ layer_normalization_1           │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=3571837)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3571837)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3571837)[0m │ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=3571837)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3571837)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=3571837)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=3571837)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3571837)[0m │ dropout_2 (Dropout)             │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=3571837)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3571837)[0m │ dense (Dense)                   │ (None, 15)             │           495 │
[36m(train_cnn_ray_tune pid=3571837)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=3571837)[0m  Total params: 45,807 (178.93 KB)
[36m(train_cnn_ray_tune pid=3571837)[0m  Trainable params: 45,807 (178.93 KB)
[36m(train_cnn_ray_tune pid=3571837)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=3571845)[0m Epoch 1/96
[36m(train_cnn_ray_tune pid=3571816)[0m  Total params: 409,231 (1.56 MB)
[36m(train_cnn_ray_tune pid=3571816)[0m  Trainable params: 409,231 (1.56 MB)
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:30[0m 2s/step - accuracy: 0.0312 - loss: 4.3342
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m  4/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.0505 - loss: 4.1617
[1m  8/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.0596 - loss: 4.0947
[36m(train_cnn_ray_tune pid=3571846)[0m 
[1m  4/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.0938 - loss: 4.0779 
[1m  9/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 13ms/step - accuracy: 0.0916 - loss: 4.0900
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m 13/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step - accuracy: 0.0634 - loss: 4.0632
[1m 17/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step - accuracy: 0.0639 - loss: 4.0480
[36m(train_cnn_ray_tune pid=3571846)[0m 
[1m 13/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.0900 - loss: 4.0953
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step - accuracy: 0.0647 - loss: 4.0346
[1m 24/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step - accuracy: 0.0651 - loss: 4.0261
[36m(train_cnn_ray_tune pid=3571846)[0m 
[1m 17/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.0897 - loss: 4.0843
[36m(train_cnn_ray_tune pid=3571846)[0m 
[1m 21/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.0897 - loss: 4.0706
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m 27/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step - accuracy: 0.0654 - loss: 4.0189
[1m 31/137[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step - accuracy: 0.0658 - loss: 4.0103
[36m(train_cnn_ray_tune pid=3571837)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:25[0m 3s/step - accuracy: 0.0938 - loss: 4.2990
[1m  5/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 18ms/step - accuracy: 0.0947 - loss: 4.2151 
[36m(train_cnn_ray_tune pid=3571844)[0m 
[1m  4/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 24ms/step - accuracy: 0.0996 - loss: 3.9777
[1m  7/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 23ms/step - accuracy: 0.0945 - loss: 4.0134
[36m(train_cnn_ray_tune pid=3571844)[0m 
[1m  9/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 24ms/step - accuracy: 0.0944 - loss: 4.0291
[1m 11/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 25ms/step - accuracy: 0.0927 - loss: 4.0481
[36m(train_cnn_ray_tune pid=3571844)[0m 
[1m 14/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 24ms/step - accuracy: 0.0907 - loss: 4.0691
[1m 17/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 23ms/step - accuracy: 0.0900 - loss: 4.0846
[36m(train_cnn_ray_tune pid=3571823)[0m 
[1m  7/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 34ms/step - accuracy: 0.0845 - loss: 4.3088
[36m(train_cnn_ray_tune pid=3571825)[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=3571825)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │[32m [repeated 112x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 206x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m │ layer_normalization             │ (None, 3, 32)          │            64 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 56x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m │ dropout (Dropout)               │ (None, 3, 32)          │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m │ dropout_3 (Dropout)             │ (None, 32)             │             0 │[32m [repeated 56x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m │ dense (Dense)                   │ (None, 15)             │           495 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m  Total params: 51,023 (199.31 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m  Trainable params: 51,023 (199.31 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571852)[0m Epoch 1/109[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m 
[1m 47/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 33ms/step - accuracy: 0.0756 - loss: 3.8187
[1m 49/137[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 33ms/step - accuracy: 0.0756 - loss: 3.8150
[1m 51/137[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 33ms/step - accuracy: 0.0756 - loss: 3.8111
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 28ms/step - accuracy: 0.0746 - loss: 3.8594 - val_accuracy: 0.1739 - val_loss: 2.4357
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:50[0m 6s/step - accuracy: 0.0625 - loss: 4.5676[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m 36/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 31ms/step - accuracy: 0.0883 - loss: 3.5170
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m Epoch 2/124[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m Epoch 2/67[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 3/127[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 16:17:17. 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_71e21    RUNNING            4   adam            tanh                                  128                128                  5          1.79438e-05         68 │
│ trial_71e21    RUNNING            4   adam            relu                                   64                 64                  5          0.00312024         137 │
│ trial_71e21    RUNNING            4   rmsprop         tanh                                  128                 64                  5          0.00257087          67 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.000287941         75 │
│ trial_71e21    RUNNING            4   rmsprop         tanh                                  128                 32                  5          0.000330218        109 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                 32                  3          0.00114134         130 │
│ trial_71e21    RUNNING            2   adam            tanh                                   32                 64                  5          0.00169955         147 │
│ trial_71e21    RUNNING            2   adam            relu                                  128                 64                  3          0.00249985         124 │
│ trial_71e21    RUNNING            3   rmsprop         relu                                   32                 32                  5          0.000412915         95 │
│ trial_71e21    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00159421          98 │
│ trial_71e21    RUNNING            3   adam            tanh                                   32                 64                  5          0.000334848        109 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                  128                 32                  5          0.000813557         72 │
│ trial_71e21    RUNNING            2   rmsprop         tanh                                   32                 32                  5          0.00267766          55 │
│ trial_71e21    RUNNING            2   adam            tanh                                   64                128                  3          0.00239804          79 │
│ trial_71e21    RUNNING            4   rmsprop         relu                                   64                 32                  3          0.000672            95 │
│ trial_71e21    RUNNING            2   rmsprop         tanh                                  128                 32                  3          0.000317831         96 │
│ trial_71e21    RUNNING            2   adam            tanh                                   64                 32                  5          2.55186e-05        127 │
│ trial_71e21    RUNNING            2   adam            tanh                                  128                128                  5          8.93848e-05         65 │
│ trial_71e21    RUNNING            4   adam            relu                                  128                 64                  3          4.42373e-05         52 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                128                  3          0.000391262        148 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[1m  3/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 56ms/step - accuracy: 0.1467 - loss: 2.3749[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 134ms/step - accuracy: 0.0859 - loss: 3.9529
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[36m(train_cnn_ray_tune pid=3571817)[0m Epoch 3/52[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 35ms/step - accuracy: 0.1086 - loss: 2.8737 - val_accuracy: 0.2551 - val_loss: 2.3036[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
[1m180/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.0815 - loss: 3.8340
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 4/65[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m Epoch 8/96[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m Epoch 6/98[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m Epoch 3/109[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 16:17:47. Total running time: 1min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING            4   adam            tanh                                  128                128                  5          1.79438e-05         68 │
│ trial_71e21    RUNNING            4   adam            relu                                   64                 64                  5          0.00312024         137 │
│ trial_71e21    RUNNING            4   rmsprop         tanh                                  128                 64                  5          0.00257087          67 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.000287941         75 │
│ trial_71e21    RUNNING            4   rmsprop         tanh                                  128                 32                  5          0.000330218        109 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                 32                  3          0.00114134         130 │
│ trial_71e21    RUNNING            2   adam            tanh                                   32                 64                  5          0.00169955         147 │
│ trial_71e21    RUNNING            2   adam            relu                                  128                 64                  3          0.00249985         124 │
│ trial_71e21    RUNNING            3   rmsprop         relu                                   32                 32                  5          0.000412915         95 │
│ trial_71e21    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00159421          98 │
│ trial_71e21    RUNNING            3   adam            tanh                                   32                 64                  5          0.000334848        109 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                  128                 32                  5          0.000813557         72 │
│ trial_71e21    RUNNING            2   rmsprop         tanh                                   32                 32                  5          0.00267766          55 │
│ trial_71e21    RUNNING            2   adam            tanh                                   64                128                  3          0.00239804          79 │
│ trial_71e21    RUNNING            4   rmsprop         relu                                   64                 32                  3          0.000672            95 │
│ trial_71e21    RUNNING            2   rmsprop         tanh                                  128                 32                  3          0.000317831         96 │
│ trial_71e21    RUNNING            2   adam            tanh                                   64                 32                  5          2.55186e-05        127 │
│ trial_71e21    RUNNING            2   adam            tanh                                  128                128                  5          8.93848e-05         65 │
│ trial_71e21    RUNNING            4   adam            relu                                  128                 64                  3          4.42373e-05         52 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                128                  3          0.000391262        148 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 7/65[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m Epoch 4/95[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m Epoch 12/124[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m Epoch 6/68[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m Epoch 14/124[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m Epoch 12/109[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 16:18:17. Total running time: 1min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING            4   adam            tanh                                  128                128                  5          1.79438e-05         68 │
│ trial_71e21    RUNNING            4   adam            relu                                   64                 64                  5          0.00312024         137 │
│ trial_71e21    RUNNING            4   rmsprop         tanh                                  128                 64                  5          0.00257087          67 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.000287941         75 │
│ trial_71e21    RUNNING            4   rmsprop         tanh                                  128                 32                  5          0.000330218        109 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                 32                  3          0.00114134         130 │
│ trial_71e21    RUNNING            2   adam            tanh                                   32                 64                  5          0.00169955         147 │
│ trial_71e21    RUNNING            2   adam            relu                                  128                 64                  3          0.00249985         124 │
│ trial_71e21    RUNNING            3   rmsprop         relu                                   32                 32                  5          0.000412915         95 │
│ trial_71e21    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00159421          98 │
│ trial_71e21    RUNNING            3   adam            tanh                                   32                 64                  5          0.000334848        109 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                  128                 32                  5          0.000813557         72 │
│ trial_71e21    RUNNING            2   rmsprop         tanh                                   32                 32                  5          0.00267766          55 │
│ trial_71e21    RUNNING            2   adam            tanh                                   64                128                  3          0.00239804          79 │
│ trial_71e21    RUNNING            4   rmsprop         relu                                   64                 32                  3          0.000672            95 │
│ trial_71e21    RUNNING            2   rmsprop         tanh                                  128                 32                  3          0.000317831         96 │
│ trial_71e21    RUNNING            2   adam            tanh                                   64                 32                  5          2.55186e-05        127 │
│ trial_71e21    RUNNING            2   adam            tanh                                  128                128                  5          8.93848e-05         65 │
│ trial_71e21    RUNNING            4   adam            relu                                  128                 64                  3          4.42373e-05         52 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                128                  3          0.000391262        148 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[1m  7/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.1818 - loss: 2.3317
[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m Epoch 10/52[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m Epoch 15/72[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m Epoch 8/68[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m Epoch 12/67[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m Epoch 13/98[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 98ms/step - accuracy: 0.1172 - loss: 2.3473[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3571823)[0m 
[1m304/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 38ms/step - accuracy: 0.1831 - loss: 2.3507
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[36m(train_cnn_ray_tune pid=3571825)[0m 
[1m 17/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 38ms/step - accuracy: 0.2289 - loss: 2.1054[32m [repeated 394x across cluster][0m
[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m Epoch 13/52[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m6s[0m 80ms/step - accuracy: 0.0952 - loss: 4.0629
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m5s[0m 80ms/step - accuracy: 0.0952 - loss: 4.0631[32m [repeated 348x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-11-05 16:18:47. Total running time: 2min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING            4   adam            tanh                                  128                128                  5          1.79438e-05         68 │
│ trial_71e21    RUNNING            4   adam            relu                                   64                 64                  5          0.00312024         137 │
│ trial_71e21    RUNNING            4   rmsprop         tanh                                  128                 64                  5          0.00257087          67 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                 32                  5          0.000287941         75 │
│ trial_71e21    RUNNING            4   rmsprop         tanh                                  128                 32                  5          0.000330218        109 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                 32                  3          0.00114134         130 │
│ trial_71e21    RUNNING            2   adam            tanh                                   32                 64                  5          0.00169955         147 │
│ trial_71e21    RUNNING            2   adam            relu                                  128                 64                  3          0.00249985         124 │
│ trial_71e21    RUNNING            3   rmsprop         relu                                   32                 32                  5          0.000412915         95 │
│ trial_71e21    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00159421          98 │
│ trial_71e21    RUNNING            3   adam            tanh                                   32                 64                  5          0.000334848        109 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                  128                 32                  5          0.000813557         72 │
│ trial_71e21    RUNNING            2   rmsprop         tanh                                   32                 32                  5          0.00267766          55 │
│ trial_71e21    RUNNING            2   adam            tanh                                   64                128                  3          0.00239804          79 │
│ trial_71e21    RUNNING            4   rmsprop         relu                                   64                 32                  3          0.000672            95 │
│ trial_71e21    RUNNING            2   rmsprop         tanh                                  128                 32                  3          0.000317831         96 │
│ trial_71e21    RUNNING            2   adam            tanh                                   64                 32                  5          2.55186e-05        127 │
│ trial_71e21    RUNNING            2   adam            tanh                                  128                128                  5          8.93848e-05         65 │
│ trial_71e21    RUNNING            4   adam            relu                                  128                 64                  3          4.42373e-05         52 │
│ trial_71e21    RUNNING            3   rmsprop         tanh                                   32                128                  3          0.000391262        148 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571816)[0m 
[1m 65/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m5s[0m 80ms/step - accuracy: 0.0950 - loss: 4.0639[32m [repeated 347x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m 
[1m  3/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2457 - loss: 2.1790  
[1m  5/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 29ms/step - accuracy: 0.2374 - loss: 2.1712
[36m(train_cnn_ray_tune pid=3571838)[0m 
[1m173/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - accuracy: 0.2370 - loss: 2.1051
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m  7/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.2105 - loss: 2.2675
[1m  9/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2088 - loss: 2.2670
[1m 11/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2071 - loss: 2.2685[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3571850)[0m Epoch 21/124[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m Epoch 10/68[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m Epoch 23/124[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m Epoch 10/137[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m Epoch 7/109[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[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=3571850)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571825)[0m 2025-11-05 16:16:50.681623: 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=3571850)[0m 2025-11-05 16:16:50.644615: 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=3571850)[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=3571850)[0m E0000 00:00:1762355810.682735 3573106 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=3571850)[0m E0000 00:00:1762355810.690317 3573106 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=3571825)[0m W0000 00:00:1762355810.760906 3573125 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=3571825)[0m 2025-11-05 16:16:50.766384: 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=3571825)[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=3571850)[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=3571850)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571850)[0m 2025-11-05 16:16:54.034755: 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=3571850)[0m 2025-11-05 16:16:54.034893: 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=3571850)[0m 2025-11-05 16:16:54.034911: 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=3571850)[0m 2025-11-05 16:16:54.034920: 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=3571850)[0m 2025-11-05 16:16:54.034932: 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=3571850)[0m 2025-11-05 16:16:54.034941: 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=3571850)[0m 2025-11-05 16:16:54.035628: 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=3571850)[0m 2025-11-05 16:16:54.035693: 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=3571850)[0m 2025-11-05 16:16:54.035697: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
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[36m(train_cnn_ray_tune pid=3571850)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:19:13. Total running time: 2min 27s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             143.768 │
│ time_total_s                 143.768 │
│ training_iteration                 1 │
│ val_accuracy                 0.38538 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:19:13. Total running time: 2min 27s
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m176/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 36ms/step - accuracy: 0.1743 - loss: 2.1488
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[36m(train_cnn_ray_tune pid=3571836)[0m 
[1m  3/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 36ms/step - accuracy: 0.1202 - loss: 2.3671  
[1m  4/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 41ms/step - accuracy: 0.1204 - loss: 2.3530
[1m  5/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 44ms/step - accuracy: 0.1214 - loss: 2.3476
[36m(train_cnn_ray_tune pid=3571849)[0m 
[1m194/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.1698 - loss: 2.3106 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 17/65[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
[1m  3/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 39ms/step - accuracy: 0.1758 - loss: 2.7533  
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 86ms/step - accuracy: 0.1406 - loss: 2.2368
[1m  4/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.1569 - loss: 2.2111 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 41ms/step - accuracy: 0.2166 - loss: 2.1371 - val_accuracy: 0.3043 - val_loss: 1.7516[32m [repeated 6x across cluster][0m

Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-05 16:19:17. Total running time: 2min 30s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING              4   adam            tanh                                  128                128                  5          1.79438e-05         68                                              │
│ trial_71e21    RUNNING              4   adam            relu                                   64                 64                  5          0.00312024         137                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 64                  5          0.00257087          67                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  3          0.00114134         130                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   32                 64                  5          0.00169955         147                                              │
│ trial_71e21    RUNNING              3   rmsprop         relu                                   32                 32                  5          0.000412915         95                                              │
│ trial_71e21    RUNNING              2   rmsprop         relu                                   64                 64                  3          0.00159421          98                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                  128                 32                  5          0.000813557         72                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                   32                 32                  5          0.00267766          55                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                128                  3          0.00239804          79                                              │
│ trial_71e21    RUNNING              4   rmsprop         relu                                   64                 32                  3          0.000672            95                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                  128                 32                  3          0.000317831         96                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                  128                128                  5          8.93848e-05         65                                              │
│ trial_71e21    RUNNING              4   adam            relu                                  128                 64                  3          4.42373e-05         52                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
[1m 24/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
[1m 72/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571816)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m Epoch 9/95[32m [repeated 12x across cluster][0m

Trial trial_71e21 finished iteration 1 at 2025-11-05 16:19:25. Total running time: 2min 38s
[36m(train_cnn_ray_tune pid=3571816)[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=3571816)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             155.081 │
│ time_total_s                 155.081 │
│ training_iteration                 1 │
│ val_accuracy                 0.14447 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:19:25. Total running time: 2min 38s
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m Epoch 12/137[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 20/65[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m Epoch 14/79[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m Epoch 21/52[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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Trial status: 2 TERMINATED | 18 RUNNING
Current time: 2025-11-05 16:19:47. Total running time: 3min 0s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING              4   adam            relu                                   64                 64                  5          0.00312024         137                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 64                  5          0.00257087          67                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  3          0.00114134         130                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   32                 64                  5          0.00169955         147                                              │
│ trial_71e21    RUNNING              3   rmsprop         relu                                   32                 32                  5          0.000412915         95                                              │
│ trial_71e21    RUNNING              2   rmsprop         relu                                   64                 64                  3          0.00159421          98                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                  128                 32                  5          0.000813557         72                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                   32                 32                  5          0.00267766          55                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                128                  3          0.00239804          79                                              │
│ trial_71e21    RUNNING              4   rmsprop         relu                                   64                 32                  3          0.000672            95                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                  128                 32                  3          0.000317831         96                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                  128                128                  5          8.93848e-05         65                                              │
│ trial_71e21    RUNNING              4   adam            relu                                  128                 64                  3          4.42373e-05         52                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m Epoch 31/72[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m Epoch 32/72[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[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=3571846)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571846)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:19:58. Total running time: 3min 11s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             189.092 │
│ time_total_s                 189.092 │
│ training_iteration                 1 │
│ val_accuracy                 0.41462 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:19:58. Total running time: 3min 12s
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m Epoch 23/67[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m Epoch 18/95[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[1m  7/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2176 - loss: 2.1076[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m Epoch 46/96[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[1m  4/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2323 - loss: 2.1807 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[1m229/274[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 20ms/step - accuracy: 0.1044 - loss: 3.0741[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 26ms/step - accuracy: 0.2386 - loss: 2.1472 - val_accuracy: 0.3231 - val_loss: 1.8039[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 107ms/step - accuracy: 0.0938 - loss: 2.8439[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step  
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m14/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=3571827)[0m 
[1m  3/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 34ms/step - accuracy: 0.1949 - loss: 2.3644 
[1m  5/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 35ms/step - accuracy: 0.1985 - loss: 2.3993[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
[1m  5/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.2436 - loss: 2.1093 [32m [repeated 21x across cluster][0m
[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[1m26/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 14ms/step
[1m44/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m52/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 13ms/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m62/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m Epoch 25/52[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3571815)[0m 
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[1m  6/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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Trial status: 3 TERMINATED | 17 RUNNING
Current time: 2025-11-05 16:20:17. Total running time: 3min 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_71e21    RUNNING              4   adam            relu                                   64                 64                  5          0.00312024         137                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 64                  5          0.00257087          67                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  3          0.00114134         130                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   32                 64                  5          0.00169955         147                                              │
│ trial_71e21    RUNNING              3   rmsprop         relu                                   32                 32                  5          0.000412915         95                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                  128                 32                  5          0.000813557         72                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                   32                 32                  5          0.00267766          55                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                128                  3          0.00239804          79                                              │
│ trial_71e21    RUNNING              4   rmsprop         relu                                   64                 32                  3          0.000672            95                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                  128                 32                  3          0.000317831         96                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                  128                128                  5          8.93848e-05         65                                              │
│ trial_71e21    RUNNING              4   adam            relu                                  128                 64                  3          4.42373e-05         52                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m137/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 13ms/step
[1m142/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m146/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[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=3571815)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
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[36m(train_cnn_ray_tune pid=3571815)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3571825)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 273ms/step
[1m 8/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step   

Trial trial_71e21 finished iteration 1 at 2025-11-05 16:20:18. Total running time: 3min 31s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             208.088 │
│ time_total_s                 208.088 │
│ training_iteration                 1 │
│ val_accuracy                 0.32431 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:20:18. Total running time: 3min 31s
[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[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=3571825)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m Epoch 49/96[32m [repeated 8x across cluster][0m
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:20:20. Total running time: 3min 33s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             210.599 │
│ time_total_s                 210.599 │
│ training_iteration                 1 │
│ val_accuracy                 0.26245 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:20:20. Total running time: 3min 33s
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571825)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m Epoch 19/79[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m Epoch 21/95[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
[1m  4/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1476 - loss: 2.3455 
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[36m(train_cnn_ray_tune pid=3571845)[0m Epoch 54/96[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 31/65[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 32/65[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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Trial status: 5 TERMINATED | 15 RUNNING
Current time: 2025-11-05 16:20:47. Total running time: 4min 0s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING              4   adam            relu                                   64                 64                  5          0.00312024         137                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  3          0.00114134         130                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   32                 64                  5          0.00169955         147                                              │
│ trial_71e21    RUNNING              3   rmsprop         relu                                   32                 32                  5          0.000412915         95                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                   32                 32                  5          0.00267766          55                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                128                  3          0.00239804          79                                              │
│ trial_71e21    RUNNING              4   rmsprop         relu                                   64                 32                  3          0.000672            95                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                  128                 32                  3          0.000317831         96                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                  128                128                  5          8.93848e-05         65                                              │
│ trial_71e21    RUNNING              4   adam            relu                                  128                 64                  3          4.42373e-05         52                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m Epoch 16/147[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 34/65[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 35/65[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 36/65[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 36/127[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m Epoch 67/96[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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Trial status: 5 TERMINATED | 15 RUNNING
Current time: 2025-11-05 16:21:17. Total running time: 4min 30s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING              4   adam            relu                                   64                 64                  5          0.00312024         137                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  3          0.00114134         130                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   32                 64                  5          0.00169955         147                                              │
│ trial_71e21    RUNNING              3   rmsprop         relu                                   32                 32                  5          0.000412915         95                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                   32                 32                  5          0.00267766          55                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                128                  3          0.00239804          79                                              │
│ trial_71e21    RUNNING              4   rmsprop         relu                                   64                 32                  3          0.000672            95                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                  128                 32                  3          0.000317831         96                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                  128                128                  5          8.93848e-05         65                                              │
│ trial_71e21    RUNNING              4   adam            relu                                  128                 64                  3          4.42373e-05         52                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 38/127[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 39/127[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m Epoch 49/109[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m Epoch 50/109[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[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=3571845)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571845)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m Epoch 19/95[32m [repeated 10x across cluster][0m
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571845)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:21:41. Total running time: 4min 54s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             292.101 │
│ time_total_s                 292.101 │
│ training_iteration                 1 │
│ val_accuracy                 0.31739 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:21:41. Total running time: 4min 54s
[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m Epoch 17/109[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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Trial status: 6 TERMINATED | 14 RUNNING
Current time: 2025-11-05 16:21:47. Total running time: 5min 0s
Logical resource usage: 14.0/20 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_71e21    RUNNING              4   adam            relu                                   64                 64                  5          0.00312024         137                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  3          0.00114134         130                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   32                 64                  5          0.00169955         147                                              │
│ trial_71e21    RUNNING              3   rmsprop         relu                                   32                 32                  5          0.000412915         95                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                   32                 32                  5          0.00267766          55                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                128                  3          0.00239804          79                                              │
│ trial_71e21    RUNNING              4   rmsprop         relu                                   64                 32                  3          0.000672            95                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                  128                128                  5          8.93848e-05         65                                              │
│ trial_71e21    RUNNING              4   adam            relu                                  128                 64                  3          4.42373e-05         52                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.000317831         96        1            292.101         0.317391 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
[1m 98/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m8s[0m 19ms/step - accuracy: 0.2381 - loss: 2.1354
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
[1m 46/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 18ms/step - accuracy: 0.1851 - loss: 2.2429[32m [repeated 204x across cluster][0m
[36m(train_cnn_ray_tune pid=3571823)[0m 
[1m117/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 24ms/step - accuracy: 0.3124 - loss: 1.8483
[1m120/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 24ms/step - accuracy: 0.3124 - loss: 1.8484[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3571851)[0m Epoch 30/79[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 88ms/step - accuracy: 0.2109 - loss: 2.2380
[1m  3/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2253 - loss: 2.2539 [32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
[1m  5/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.1817 - loss: 2.0170 [32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571847)[0m 
[1m206/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 14ms/step - accuracy: 0.2449 - loss: 2.0040[32m [repeated 199x across cluster][0m
[36m(train_cnn_ray_tune pid=3571817)[0m Epoch 41/52[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 47/65[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 48/65[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m Epoch 22/75[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571849)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 72ms/step - accuracy: 0.2500 - loss: 2.1334
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[36m(train_cnn_ray_tune pid=3571847)[0m 
[1m  6/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 12ms/step - accuracy: 0.2719 - loss: 1.9861 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 92ms/step - accuracy: 0.1719 - loss: 2.2717[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[1m 79/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 21ms/step - accuracy: 0.2486 - loss: 2.0648[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 322ms/step
[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m 7/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step  
[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m18/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m66/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=3571823)[0m Epoch 18/148[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3571824)[0m 
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[1m  7/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step 
[1m 12/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step
[36m(train_cnn_ray_tune pid=3571848)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 69ms/step - accuracy: 0.3125 - loss: 1.9295
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[36m(train_cnn_ray_tune pid=3571827)[0m 
[1m  3/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 32ms/step - accuracy: 0.2687 - loss: 2.1363 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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Trial status: 6 TERMINATED | 14 RUNNING
Current time: 2025-11-05 16:22:17. Total running time: 5min 30s
Logical resource usage: 14.0/20 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_71e21    RUNNING              4   adam            relu                                   64                 64                  5          0.00312024         137                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  3          0.00114134         130                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   32                 64                  5          0.00169955         147                                              │
│ trial_71e21    RUNNING              3   rmsprop         relu                                   32                 32                  5          0.000412915         95                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                   32                 32                  5          0.00267766          55                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                128                  3          0.00239804          79                                              │
│ trial_71e21    RUNNING              4   rmsprop         relu                                   64                 32                  3          0.000672            95                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                  128                128                  5          8.93848e-05         65                                              │
│ trial_71e21    RUNNING              4   adam            relu                                  128                 64                  3          4.42373e-05         52                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.000317831         96        1            292.101         0.317391 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[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=3571824)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
[1m143/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 10ms/step
[1m150/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571824)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:22:18. Total running time: 5min 31s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             328.693 │
│ time_total_s                 328.693 │
│ training_iteration                 1 │
│ val_accuracy                 0.19763 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:22:18. Total running time: 5min 31s
[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571851)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:22:20. Total running time: 5min 33s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             330.396 │
│ time_total_s                 330.396 │
│ training_iteration                 1 │
│ val_accuracy                 0.37332 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:22:20. Total running time: 5min 33s
[36m(train_cnn_ray_tune pid=3571836)[0m Epoch 61/109[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571851)[0m 
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[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
[1m  4/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.2192 - loss: 2.2368 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 82ms/step - accuracy: 0.2500 - loss: 2.0936
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m Epoch 31/137[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571848)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[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=3571848)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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[36m(train_cnn_ray_tune pid=3571848)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:22:28. Total running time: 5min 41s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              338.22 │
│ time_total_s                  338.22 │
│ training_iteration                 1 │
│ val_accuracy                 0.24644 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:22:28. Total running time: 5min 41s
[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m Epoch 32/137[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[1m  8/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.1216 - loss: 2.6476
[36m(train_cnn_ray_tune pid=3571838)[0m Epoch 26/147[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m Epoch 67/109[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
[1m 50/137[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.2816 - loss: 2.0353
[1m 53/137[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.2814 - loss: 2.0357[32m [repeated 308x across cluster][0m
[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
[1m338/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 17ms/step - accuracy: 0.3120 - loss: 1.8095
[1m341/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 17ms/step - accuracy: 0.3120 - loss: 1.8095
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 58/65[32m [repeated 8x across cluster][0m

Trial status: 9 TERMINATED | 11 RUNNING
Current time: 2025-11-05 16:22:47. Total running time: 6min 0s
Logical resource usage: 11.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING              4   adam            relu                                   64                 64                  5          0.00312024         137                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  3          0.00114134         130                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   32                 64                  5          0.00169955         147                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              2   rmsprop         tanh                                   32                 32                  5          0.00267766          55                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                  128                128                  5          8.93848e-05         65                                              │
│ trial_71e21    RUNNING              4   adam            relu                                  128                 64                  3          4.42373e-05         52                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           3   rmsprop         relu                                   32                 32                  5          0.000412915         95        1            338.22          0.246443 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   64                128                  3          0.00239804          79        1            330.396         0.37332  │
│ trial_71e21    TERMINATED           4   rmsprop         relu                                   64                 32                  3          0.000672            95        1            328.693         0.197628 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.000317831         96        1            292.101         0.317391 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571847)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 13ms/step - accuracy: 0.2652 - loss: 1.9804 - val_accuracy: 0.3342 - val_loss: 1.6282[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 355ms/step
[1m 9/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step   
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m37/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m52/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 7ms/step
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 7ms/step
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3571852)[0m 
[1m295/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 16ms/step - accuracy: 0.2610 - loss: 1.9952
[1m299/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 16ms/step - accuracy: 0.2610 - loss: 1.9951[32m [repeated 337x across cluster][0m
[36m(train_cnn_ray_tune pid=3571849)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36s[0m 67ms/step - accuracy: 0.2188 - loss: 2.2554[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m  9/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step 
[1m 17/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
[1m114/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 7ms/step
[1m121/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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[36m(train_cnn_ray_tune pid=3571817)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:22:50. Total running time: 6min 3s
[36m(train_cnn_ray_tune pid=3571817)[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=3571817)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             360.588 │
│ time_total_s                 360.588 │
│ training_iteration                 1 │
│ val_accuracy                 0.17233 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:22:50. Total running time: 6min 3s
[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 57/127[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[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=3571847)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571847)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:22:56. Total running time: 6min 9s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             366.576 │
│ time_total_s                 366.576 │
│ training_iteration                 1 │
│ val_accuracy                 0.33458 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:22:56. Total running time: 6min 9s
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:23:00. Total running time: 6min 13s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              370.61 │
│ time_total_s                  370.61 │
│ training_iteration                 1 │
│ val_accuracy                 0.35296 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571837)[0m 
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Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:23:00. Total running time: 6min 13s
[36m(train_cnn_ray_tune pid=3571849)[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=3571849)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3571838)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m Epoch 63/65[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:23:04. Total running time: 6min 17s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             374.493 │
│ time_total_s                 374.493 │
│ training_iteration                 1 │
│ val_accuracy                 0.24506 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571837)[0m 
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Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:23:04. Total running time: 6min 17s
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571849)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571814)[0m 
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[1m 12/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step 
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 62/127[32m [repeated 8x across cluster][0m

Trial trial_71e21 finished iteration 1 at 2025-11-05 16:23:07. Total running time: 6min 20s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             377.874 │
│ time_total_s                 377.874 │
│ training_iteration                 1 │
│ val_accuracy                 0.36225 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:23:07. Total running time: 6min 20s
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step   
[36m(train_cnn_ray_tune pid=3571827)[0m 
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[1m 18/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step 
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:23:09. Total running time: 6min 22s
[36m(train_cnn_ray_tune pid=3571827)[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=3571827)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             379.642 │
│ time_total_s                 379.642 │
│ training_iteration                 1 │
│ val_accuracy                  0.3666 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:23:09. Total running time: 6min 22s
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571827)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m Epoch 26/109[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m Epoch 84/109[32m [repeated 10x across cluster][0m

Trial status: 15 TERMINATED | 5 RUNNING
Current time: 2025-11-05 16:23:17. Total running time: 6min 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_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              4   rmsprop         tanh                                  128                 32                  5          0.000330218        109                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   adam            relu                                   64                 64                  5          0.00312024         137        1            377.874         0.362253 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                   32                 32                  3          0.00114134         130        1            374.493         0.245059 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00169955         147        1            370.61          0.352964 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           3   rmsprop         relu                                   32                 32                  5          0.000412915         95        1            338.22          0.246443 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                   32                 32                  5          0.00267766          55        1            366.576         0.334585 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   64                128                  3          0.00239804          79        1            330.396         0.37332  │
│ trial_71e21    TERMINATED           4   rmsprop         relu                                   64                 32                  3          0.000672            95        1            328.693         0.197628 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.000317831         96        1            292.101         0.317391 │
│ trial_71e21    TERMINATED           2   adam            tanh                                  128                128                  5          8.93848e-05         65        1            379.642         0.366601 │
│ trial_71e21    TERMINATED           4   adam            relu                                  128                 64                  3          4.42373e-05         52        1            360.588         0.172332 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m Epoch 88/109[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[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=3571836)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571836)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:23:27. Total running time: 6min 40s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             397.446 │
│ time_total_s                 397.446 │
│ training_iteration                 1 │
│ val_accuracy                 0.26423 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:23:27. Total running time: 6min 40s
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 75/127[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 90/127[32m [repeated 8x across cluster][0m

Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-05 16:23:47. Total running time: 7min 0s
Logical resource usage: 4.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   adam            relu                                   64                 64                  5          0.00312024         137        1            377.874         0.362253 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 32                  5          0.000330218        109        1            397.446         0.264229 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                   32                 32                  3          0.00114134         130        1            374.493         0.245059 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00169955         147        1            370.61          0.352964 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           3   rmsprop         relu                                   32                 32                  5          0.000412915         95        1            338.22          0.246443 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                   32                 32                  5          0.00267766          55        1            366.576         0.334585 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   64                128                  3          0.00239804          79        1            330.396         0.37332  │
│ trial_71e21    TERMINATED           4   rmsprop         relu                                   64                 32                  3          0.000672            95        1            328.693         0.197628 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.000317831         96        1            292.101         0.317391 │
│ trial_71e21    TERMINATED           2   adam            tanh                                  128                128                  5          8.93848e-05         65        1            379.642         0.366601 │
│ trial_71e21    TERMINATED           4   adam            relu                                  128                 64                  3          4.42373e-05         52        1            360.588         0.172332 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m Epoch 35/148[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m Epoch 46/75[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m Epoch 48/75[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m Epoch 50/75[32m [repeated 9x across cluster][0m
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m Epoch 41/148[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 114/127[32m [repeated 9x across cluster][0m
Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-05 16:24:17. Total running time: 7min 31s
Logical resource usage: 4.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              2   adam            tanh                                   64                 32                  5          2.55186e-05        127                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   adam            relu                                   64                 64                  5          0.00312024         137        1            377.874         0.362253 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 32                  5          0.000330218        109        1            397.446         0.264229 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                   32                 32                  3          0.00114134         130        1            374.493         0.245059 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00169955         147        1            370.61          0.352964 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           3   rmsprop         relu                                   32                 32                  5          0.000412915         95        1            338.22          0.246443 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                   32                 32                  5          0.00267766          55        1            366.576         0.334585 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   64                128                  3          0.00239804          79        1            330.396         0.37332  │
│ trial_71e21    TERMINATED           4   rmsprop         relu                                   64                 32                  3          0.000672            95        1            328.693         0.197628 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.000317831         96        1            292.101         0.317391 │
│ trial_71e21    TERMINATED           2   adam            tanh                                  128                128                  5          8.93848e-05         65        1            379.642         0.366601 │
│ trial_71e21    TERMINATED           4   adam            relu                                  128                 64                  3          4.42373e-05         52        1            360.588         0.172332 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[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=3571837)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571837)[0m Epoch 126/127[32m [repeated 9x across cluster][0m
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step

Trial trial_71e21 finished iteration 1 at 2025-11-05 16:24:35. Total running time: 7min 48s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             465.899 │
│ time_total_s                 465.899 │
│ training_iteration                 1 │
│ val_accuracy                 0.24111 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:24:35. Total running time: 7min 48s
[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m Epoch 53/109[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m Epoch 55/109[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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Trial status: 17 TERMINATED | 3 RUNNING
Current time: 2025-11-05 16:24:48. Total running time: 8min 1s
Logical resource usage: 3.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                 32                  5          0.000287941         75                                              │
│ trial_71e21    RUNNING              3   adam            tanh                                   32                 64                  5          0.000334848        109                                              │
│ trial_71e21    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.000391262        148                                              │
│ trial_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   adam            relu                                   64                 64                  5          0.00312024         137        1            377.874         0.362253 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 32                  5          0.000330218        109        1            397.446         0.264229 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                   32                 32                  3          0.00114134         130        1            374.493         0.245059 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00169955         147        1            370.61          0.352964 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           3   rmsprop         relu                                   32                 32                  5          0.000412915         95        1            338.22          0.246443 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                   32                 32                  5          0.00267766          55        1            366.576         0.334585 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   64                128                  3          0.00239804          79        1            330.396         0.37332  │
│ trial_71e21    TERMINATED           4   rmsprop         relu                                   64                 32                  3          0.000672            95        1            328.693         0.197628 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.000317831         96        1            292.101         0.317391 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   64                 32                  5          2.55186e-05        127        1            465.899         0.241107 │
│ trial_71e21    TERMINATED           2   adam            tanh                                  128                128                  5          8.93848e-05         65        1            379.642         0.366601 │
│ trial_71e21    TERMINATED           4   adam            relu                                  128                 64                  3          4.42373e-05         52        1            360.588         0.172332 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[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=3571823)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m 
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[36m(train_cnn_ray_tune pid=3571844)[0m Epoch 68/75[32m [repeated 6x across cluster][0m
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:24:55. Total running time: 8min 8s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             485.211 │
│ time_total_s                 485.211 │
│ training_iteration                 1 │
│ val_accuracy                 0.41008 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:24:55. Total running time: 8min 8s
[36m(train_cnn_ray_tune pid=3571823)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:24:57. Total running time: 8min 10s
[36m(train_cnn_ray_tune pid=3571844)[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=3571844)[0m   _log_deprecation_warning(
2025-11-05 16:25:00,319	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_17_classes/ESANN_hyperparameters_tuning' in 0.0063s.
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             488.016 │
│ time_total_s                 488.016 │
│ training_iteration                 1 │
│ val_accuracy                 0.29664 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:24:57. Total running time: 8min 10s
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m Epoch 61/109[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3571844)[0m 
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Trial trial_71e21 finished iteration 1 at 2025-11-05 16:25:00. Total running time: 8min 13s
╭──────────────────────────────────────╮
│ Trial trial_71e21 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              490.26 │
│ time_total_s                  490.26 │
│ training_iteration                 1 │
│ val_accuracy                 0.35415 │
╰──────────────────────────────────────╯

Trial trial_71e21 completed after 1 iterations at 2025-11-05 16:25:00. Total running time: 8min 13s

Trial status: 20 TERMINATED
Current time: 2025-11-05 16:25:00. Total running time: 8min 13s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762356300.457608 3570196 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_71e21    TERMINATED           4   adam            tanh                                  128                128                  5          1.79438e-05         68        1            155.081         0.144466 │
│ trial_71e21    TERMINATED           4   adam            relu                                   64                 64                  5          0.00312024         137        1            377.874         0.362253 │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 64                  5          0.00257087          67        1            208.088         0.324308 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                   32                 32                  5          0.000287941         75        1            488.016         0.29664  │
│ trial_71e21    TERMINATED           4   rmsprop         tanh                                  128                 32                  5          0.000330218        109        1            397.446         0.264229 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                   32                 32                  3          0.00114134         130        1            374.493         0.245059 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00169955         147        1            370.61          0.352964 │
│ trial_71e21    TERMINATED           2   adam            relu                                  128                 64                  3          0.00249985         124        1            143.768         0.385375 │
│ trial_71e21    TERMINATED           3   rmsprop         relu                                   32                 32                  5          0.000412915         95        1            338.22          0.246443 │
│ trial_71e21    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00159421          98        1            189.092         0.414625 │
│ trial_71e21    TERMINATED           3   adam            tanh                                   32                 64                  5          0.000334848        109        1            490.26          0.35415  │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                  128                 32                  5          0.000813557         72        1            210.599         0.262451 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                   32                 32                  5          0.00267766          55        1            366.576         0.334585 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   64                128                  3          0.00239804          79        1            330.396         0.37332  │
│ trial_71e21    TERMINATED           4   rmsprop         relu                                   64                 32                  3          0.000672            95        1            328.693         0.197628 │
│ trial_71e21    TERMINATED           2   rmsprop         tanh                                  128                 32                  3          0.000317831         96        1            292.101         0.317391 │
│ trial_71e21    TERMINATED           2   adam            tanh                                   64                 32                  5          2.55186e-05        127        1            465.899         0.241107 │
│ trial_71e21    TERMINATED           2   adam            tanh                                  128                128                  5          8.93848e-05         65        1            379.642         0.366601 │
│ trial_71e21    TERMINATED           4   adam            relu                                  128                 64                  3          4.42373e-05         52        1            360.588         0.172332 │
│ trial_71e21    TERMINATED           3   rmsprop         tanh                                   32                128                  3          0.000391262        148        1            485.211         0.410079 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'rmsprop', 'funcion_activacion': 'relu', 'tamanho_minilote': 64, 'numero_filtros': 64, 'tamanho_filtro': 3, 'tasa_aprendizaje': 0.0015942083114515514, 'epochs': 98}
[36m(train_cnn_ray_tune pid=3571852)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356302.065824 3662672 service.cc:152] XLA service 0x797c8401ad30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356302.065884 3662672 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:25:02.101478: 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 00:00:1762356302.220178 3662672 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356304.503417 3662672 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.7325 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3250 - loss: 1.7454
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3290 - loss: 1.7491 - val_accuracy: 0.4154 - val_loss: 1.4730
Epoch 15/98

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[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3269 - loss: 1.7577
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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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Saved model to disk.
[36m(train_cnn_ray_tune pid=3571852)[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=3571852)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[36m(train_cnn_ray_tune pid=3571852)[0m 
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[1m 38/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 3ms/step - accuracy: 0.3001 - loss: 1.8088
[36m(train_cnn_ray_tune pid=3571852)[0m 
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[1m513/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.3066 - loss: 1.8067[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3571852)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 3ms/step - accuracy: 0.3068 - loss: 1.8067 - val_accuracy: 0.3542 - val_loss: 1.5410
[36m(train_cnn_ray_tune pid=3571852)[0m 
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=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:49[0m 859ms/step
[1m 63/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 811us/step  
[1m136/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 744us/step
[1m210/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 722us/step
[1m284/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 710us/step
[1m355/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 710us/step
[1m417/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 725us/step
[1m491/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 720us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 779us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 73/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 695us/step
[1m141/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 718us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.29 [%]
Global F1 score (validation) = 30.41 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.3122182e-03 3.9980793e-03 3.5275742e-03 ... 2.7654336e-03
  9.5736310e-03 4.5966511e-04]
 [3.4926608e-03 3.1171013e-03 2.8568197e-03 ... 3.7236453e-03
  4.4452734e-03 3.8632864e-04]
 [3.6283848e-03 3.2345976e-03 3.0283704e-03 ... 4.2817243e-03
  3.6077786e-03 3.8167706e-04]
 ...
 [1.8469995e-04 1.8171474e-04 1.5422003e-04 ... 4.8766001e-03
  4.3228930e-03 4.6876497e-03]
 [2.9361242e-04 2.9182778e-04 2.4879168e-04 ... 6.9806608e-03
  5.3541828e-03 4.4291639e-03]
 [7.1514789e-03 7.2390814e-03 8.3934022e-03 ... 2.3749880e-01
  7.5905467e-03 6.2928395e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 40.66 [%]
Global accuracy score (test) = 39.55 [%]
Global F1 score (train) = 33.79 [%]
Global F1 score (test) = 31.49 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.89      0.35       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.47      0.73      0.57       161
   DE PIE DOBLANDO TOALLAS       0.28      0.10      0.15       161
    DE PIE MOVIENDO LIBROS       0.28      0.20      0.23       161
          DE PIE USANDO PC       0.63      0.91      0.74       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       0.99      0.84      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.24      0.95      0.39       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.57      0.56      0.56       161
                    TROTAR       0.82      0.81      0.82       138

                  accuracy                           0.40      2392
                 macro avg       0.30      0.40      0.31      2392
              weighted avg       0.30      0.40      0.31      2392

2025-11-05 16:25:32.262778: 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 16:25:32.273978: 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 00:00:1762356332.286945 3665524 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356332.291087 3665524 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356332.300835 3665524 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356332.300854 3665524 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356332.300856 3665524 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356332.300857 3665524 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:25:32.303980: 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 00:00:1762356334.565265 3665524 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356336.005373 3665633 service.cc:152] XLA service 0x71f79800b060 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356336.005403 3665633 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:25:36.038573: 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 00:00:1762356336.157972 3665633 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356338.408077 3665633 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:23[0m 3s/step - accuracy: 0.0781 - loss: 4.5602
[1m 34/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0914 - loss: 4.0743  
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[1m120/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0964 - loss: 3.7700
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Epoch 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2617 - loss: 1.9341
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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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[1m 75/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3346 - loss: 1.7477
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3294 - loss: 1.7595 - val_accuracy: 0.3763 - val_loss: 1.5202
Epoch 12/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.7503 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.7620
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Epoch 13/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3188 - loss: 1.7468
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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7338
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Epoch 18/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3082 - loss: 1.7859 
[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3193 - loss: 1.7579
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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.6977
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Epoch 22/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.7199 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.7000
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[1m195/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.6914
[1m236/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.6908
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3476 - loss: 1.6899 - val_accuracy: 0.3933 - val_loss: 1.4608
Epoch 23/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.4062 - loss: 1.7736
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.6864 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.6825
[1m125/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.6810
[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.6790
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.6767
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.6754
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3470 - loss: 1.6750 - val_accuracy: 0.3625 - val_loss: 1.4934
Epoch 24/98

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[1m 45/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.7018 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.6898
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[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.6787
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.6786
[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.6783
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3505 - loss: 1.6783 - val_accuracy: 0.3840 - val_loss: 1.4677
Epoch 25/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.7095 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.6945
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Epoch 26/98

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Epoch 27/98

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Epoch 28/98

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[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3664 - loss: 1.6277
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3597 - loss: 1.6404 - val_accuracy: 0.4036 - val_loss: 1.4710
Epoch 29/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3438 - loss: 1.5045
[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.6230 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.6294
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[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.6372
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3548 - loss: 1.6394
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3555 - loss: 1.6405 - val_accuracy: 0.4115 - val_loss: 1.4547
Epoch 30/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2969 - loss: 1.6819
[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.6420 
[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.6497
[1m131/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.6508
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[1m215/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3612 - loss: 1.6521
[1m259/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.6521
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3617 - loss: 1.6521 - val_accuracy: 0.3901 - val_loss: 1.4635
Epoch 31/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.6413 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.6395
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Epoch 32/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3438 - loss: 1.5546
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[1m196/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.6464
[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.6466
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3618 - loss: 1.6469 - val_accuracy: 0.3832 - val_loss: 1.4576

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 734ms/step
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 743us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 1: 39.55 [%]
F1-score capturado en la ejecución 1: 31.49 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:58[0m 877ms/step
[1m 70/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 735us/step  
[1m141/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 720us/step
[1m210/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 723us/step
[1m277/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 730us/step
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[1m419/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 723us/step
[1m490/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 721us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 753us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 62/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 823us/step
[1m134/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 757us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 38.32 [%]
Global F1 score (validation) = 32.37 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[3.2336153e-03 4.9566356e-03 3.6063266e-03 ... 2.5577440e-03
  5.4928404e-03 3.0529607e-04]
 [2.9118508e-03 4.4972459e-03 3.6868723e-03 ... 4.0433928e-03
  2.4313626e-03 3.9946850e-04]
 [2.8682712e-03 4.4280570e-03 3.6569340e-03 ... 4.1880021e-03
  2.3464228e-03 4.1448095e-04]
 ...
 [1.9980637e-04 1.9866026e-04 2.0984486e-04 ... 3.9132279e-03
  2.8113131e-03 6.0527236e-03]
 [7.6290591e-05 7.7390592e-05 8.3627318e-05 ... 1.5642681e-03
  1.2584636e-03 5.5190562e-03]
 [6.4811250e-03 6.1337720e-03 7.2880969e-03 ... 2.6505741e-01
  7.1919109e-03 6.5970961e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 43.88 [%]
Global accuracy score (test) = 38.59 [%]
Global F1 score (train) = 37.8 [%]
Global F1 score (test) = 32.03 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.19      0.66      0.29       161
 CAMINAR CON MÓVIL O LIBRO       0.06      0.01      0.01       161
       CAMINAR USUAL SPEED       0.22      0.12      0.15       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.54      0.42      0.47       161
   DE PIE DOBLANDO TOALLAS       0.29      0.40      0.34       161
    DE PIE MOVIENDO LIBROS       0.30      0.08      0.13       161
          DE PIE USANDO PC       0.57      0.94      0.71       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       0.98      0.91      0.94       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.26      1.00      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.54      0.54       161
                    TROTAR       0.87      0.78      0.82       138

                  accuracy                           0.39      2392
                 macro avg       0.32      0.39      0.32      2392
              weighted avg       0.31      0.39      0.32      2392

2025-11-05 16:26:09.438497: 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 16:26:09.449678: 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 00:00:1762356369.462696 3669498 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356369.466827 3669498 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356369.476914 3669498 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356369.476934 3669498 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356369.476937 3669498 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356369.476938 3669498 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:26:09.480118: 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 00:00:1762356371.730870 3669498 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356373.177007 3669629 service.cc:152] XLA service 0x74c59401c9b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356373.177038 3669629 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:26:13.211149: 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 00:00:1762356373.333103 3669629 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356375.565943 3669629 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 2/98

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Epoch 3/98

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[1m199/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.1082
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Epoch 4/98

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Epoch 5/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2733 - loss: 1.9236
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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3146 - loss: 1.7829
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Epoch 11/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3181 - loss: 1.7789
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Epoch 12/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.7963 
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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7187 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3449 - loss: 1.7115
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Epoch 18/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.6922 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.6864
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Epoch 19/98

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[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7057
[1m200/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7052
[1m239/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.7048
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3422 - loss: 1.7036 - val_accuracy: 0.4126 - val_loss: 1.4364
Epoch 20/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2969 - loss: 1.7672
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[1m169/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.6675
[1m214/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.6694
[1m258/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.6713
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3554 - loss: 1.6719 - val_accuracy: 0.3974 - val_loss: 1.4629
Epoch 21/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2656 - loss: 1.8776
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[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.6721
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3521 - loss: 1.6721 - val_accuracy: 0.4012 - val_loss: 1.4615
Epoch 22/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.5312 - loss: 1.5065
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3647 - loss: 1.6450 
[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.6478
[1m128/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3590 - loss: 1.6509
[1m171/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3576 - loss: 1.6543
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.6577
[1m252/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.6607
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3551 - loss: 1.6620 - val_accuracy: 0.4178 - val_loss: 1.4516
Epoch 23/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3906 - loss: 1.5513
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.6535 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.6556
[1m122/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3553 - loss: 1.6587
[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3567 - loss: 1.6611
[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.6626
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.6632
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3575 - loss: 1.6638 - val_accuracy: 0.4032 - val_loss: 1.4627
Epoch 24/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3594 - loss: 1.6877
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3527 - loss: 1.6801 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.6742
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[1m159/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.6717
[1m199/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.6706
[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.6697
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 703ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 782us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 2: 38.59 [%]
F1-score capturado en la ejecución 2: 32.03 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:12[0m 901ms/step
[1m 72/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 706us/step  
[1m143/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 708us/step
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[1m290/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 695us/step
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 794us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 746us/step
[1m130/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 785us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 38.95 [%]
Global F1 score (validation) = 34.36 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.2167131e-03 5.5474015e-03 3.8228000e-03 ... 3.0360310e-03
  1.0679059e-02 4.4438412e-04]
 [3.7586309e-03 4.0790294e-03 2.9989535e-03 ... 4.7243889e-03
  4.9336511e-03 4.7613925e-04]
 [2.7785099e-03 3.0162053e-03 2.2546919e-03 ... 4.9065510e-03
  3.1684011e-03 3.9669374e-04]
 ...
 [3.2398492e-04 4.0208144e-04 3.4278457e-04 ... 9.4068376e-03
  4.2623202e-03 1.1888771e-02]
 [1.0078292e-04 1.2382050e-04 1.0287973e-04 ... 2.7255269e-03
  1.7035510e-03 1.1601693e-02]
 [5.5631204e-03 6.5263789e-03 7.1509788e-03 ... 2.3462312e-01
  9.0431608e-03 5.6769825e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 45.42 [%]
Global accuracy score (test) = 43.44 [%]
Global F1 score (train) = 40.84 [%]
Global F1 score (test) = 38.22 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.57      0.35       161
       CAMINAR USUAL SPEED       0.24      0.37      0.30       161
            CAMINAR ZIGZAG       0.23      0.07      0.11       161
          DE PIE BARRIENDO       0.49      0.79      0.60       161
   DE PIE DOBLANDO TOALLAS       0.09      0.01      0.01       161
    DE PIE MOVIENDO LIBROS       0.23      0.19      0.21       161
          DE PIE USANDO PC       0.57      0.91      0.70       161
        FASE REPOSO CON K5       0.42      0.86      0.56       161
INCREMENTAL CICLOERGOMETRO       0.99      0.89      0.94       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.24      0.26      0.25       161
      SENTADO VIENDO LA TV       0.38      0.29      0.33       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.65      0.58       161
                    TROTAR       0.92      0.70      0.80       138

                  accuracy                           0.43      2392
                 macro avg       0.37      0.44      0.38      2392
              weighted avg       0.37      0.43      0.38      2392

2025-11-05 16:26:42.724301: 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 16:26:42.735619: 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 00:00:1762356402.748706 3672739 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356402.752869 3672739 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356402.762875 3672739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356402.762899 3672739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356402.762903 3672739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356402.762904 3672739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:26:42.766039: 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 00:00:1762356405.018452 3672739 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356406.412001 3672846 service.cc:152] XLA service 0x7a974c01c000 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356406.412042 3672846 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:26:46.450811: 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 00:00:1762356406.575579 3672846 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356408.823875 3672846 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:21[0m 3s/step - accuracy: 0.0469 - loss: 4.4602
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0794 - loss: 4.0474  
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0852 - loss: 3.8645
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0900 - loss: 3.7340
[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0936 - loss: 3.6304
[1m206/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0970 - loss: 3.5412
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1000 - loss: 3.4655
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.1019 - loss: 3.4229
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.1019 - loss: 3.4213 - val_accuracy: 0.2547 - val_loss: 2.0781
Epoch 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3239 - loss: 1.7525
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3225 - loss: 1.7709 - val_accuracy: 0.3664 - val_loss: 1.5411
Epoch 12/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3174 - loss: 1.7841 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3204 - loss: 1.7739
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[1m168/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.7698
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[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3218 - loss: 1.7699
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3219 - loss: 1.7704 - val_accuracy: 0.3719 - val_loss: 1.5069
Epoch 13/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3230 - loss: 1.7721 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3211 - loss: 1.7685
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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3285 - loss: 1.7319 - val_accuracy: 0.3785 - val_loss: 1.4902
Epoch 18/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7000 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.6982
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[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7010
[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3430 - loss: 1.7032
[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7050
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3428 - loss: 1.7057 - val_accuracy: 0.3879 - val_loss: 1.4688
Epoch 19/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3589 - loss: 1.6467 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.6661
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Epoch 20/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.6527 
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Epoch 21/98

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Epoch 22/98

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Epoch 23/98

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Epoch 24/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3334 - loss: 1.6743 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.6615
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[1m216/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.6614
[1m258/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.6619
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.6623 - val_accuracy: 0.4105 - val_loss: 1.4830
Epoch 25/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.6948 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.6818
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[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3546 - loss: 1.6756
[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.6748
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Epoch 26/98

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[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.6578
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.6590
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3568 - loss: 1.6601 - val_accuracy: 0.3688 - val_loss: 1.4597

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 711ms/step
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 699us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 3: 43.44 [%]
F1-score capturado en la ejecución 3: 38.22 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:21[0m 918ms/step
[1m 67/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 766us/step  
[1m147/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 693us/step
[1m218/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 697us/step
[1m294/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 688us/step
[1m370/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 683us/step
[1m444/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 683us/step
[1m520/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 679us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 738us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 75/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 682us/step
[1m150/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 676us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 3ms/step
Global accuracy score (validation) = 36.88 [%]
Global F1 score (validation) = 30.01 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.8527277e-03 8.4036738e-03 4.2482731e-03 ... 3.2094875e-03
  8.6022895e-03 3.3583408e-04]
 [3.7479599e-03 5.3141336e-03 2.8056279e-03 ... 3.6981246e-03
  3.1891686e-03 2.6609551e-04]
 [4.0599266e-03 5.7602413e-03 3.0265294e-03 ... 3.7003935e-03
  3.8652946e-03 2.9704999e-04]
 ...
 [2.6527126e-04 2.0911326e-04 2.6503965e-04 ... 6.1268331e-03
  3.6835028e-03 1.9464435e-02]
 [4.7182478e-04 3.6426258e-04 4.5578266e-04 ... 8.6651491e-03
  6.5037347e-03 1.7853174e-02]
 [6.4070490e-03 6.0852785e-03 7.2577717e-03 ... 2.3811734e-01
  4.3640691e-03 8.3223125e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.46 [%]
Global accuracy score (test) = 39.13 [%]
Global F1 score (train) = 35.22 [%]
Global F1 score (test) = 33.2 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.42      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.22      0.35      0.27       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.48      0.81      0.60       161
   DE PIE DOBLANDO TOALLAS       0.70      0.04      0.08       161
    DE PIE MOVIENDO LIBROS       0.16      0.05      0.08       161
          DE PIE USANDO PC       0.43      0.88      0.58       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       0.99      0.88      0.93       161
           SENTADO LEYENDO       0.76      0.12      0.20       161
         SENTADO USANDO PC       0.33      0.14      0.20       161
      SENTADO VIENDO LA TV       0.26      0.85      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.56      0.61      0.59       161
                    TROTAR       0.86      0.77      0.81       138

                  accuracy                           0.39      2392
                 macro avg       0.40      0.39      0.33      2392
              weighted avg       0.39      0.39      0.33      2392

2025-11-05 16:27:16.955535: 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 16:27:16.967618: 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 00:00:1762356436.981697 3676142 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356436.985930 3676142 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356436.996606 3676142 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356436.996631 3676142 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356436.996634 3676142 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356436.996636 3676142 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:27:17.000030: 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 00:00:1762356439.305410 3676142 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356440.730876 3676274 service.cc:152] XLA service 0x7ccc1000a7d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356440.730907 3676274 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:27:20.764257: 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 00:00:1762356440.887694 3676274 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356443.142930 3676274 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:26[0m 3s/step - accuracy: 0.0625 - loss: 4.2986
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0791 - loss: 4.0722  
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0874 - loss: 3.9119
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[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0994 - loss: 3.6761
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Epoch 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3225 - loss: 1.7914
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[1m238/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.7924
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Epoch 12/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3324 - loss: 1.7981 
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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.6744 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.6942
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[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.7134
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3379 - loss: 1.7151 - val_accuracy: 0.3660 - val_loss: 1.4979
Epoch 18/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.6997 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.7028
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Epoch 19/98

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[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.6995
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3518 - loss: 1.6998 - val_accuracy: 0.3840 - val_loss: 1.4640
Epoch 20/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 1.6074
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[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.6891
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3466 - loss: 1.6899 - val_accuracy: 0.3763 - val_loss: 1.4768
Epoch 21/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 31ms/step - accuracy: 0.3281 - loss: 1.7193
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[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.6965
[1m128/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.6896
[1m171/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.6879
[1m214/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.6883
[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.6882
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3474 - loss: 1.6881 - val_accuracy: 0.3848 - val_loss: 1.4758
Epoch 22/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3750 - loss: 1.7867
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.6917 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.6879
[1m125/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.6844
[1m169/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.6825
[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.6823
[1m255/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.6818
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3492 - loss: 1.6817 - val_accuracy: 0.4136 - val_loss: 1.4679
Epoch 23/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2656 - loss: 1.7164
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.6574 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.6610
[1m119/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3638 - loss: 1.6654
[1m160/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.6692
[1m200/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.6713
[1m239/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3593 - loss: 1.6729
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3584 - loss: 1.6742 - val_accuracy: 0.3739 - val_loss: 1.4610

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 693ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 4: 39.13 [%]
F1-score capturado en la ejecución 4: 33.2 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 744us/step
[1m142/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 714us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.39 [%]
Global F1 score (validation) = 29.58 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.00394465 0.00538425 0.00364987 ... 0.00313298 0.0046666  0.00059245]
 [0.00393428 0.00517082 0.00374749 ... 0.00439594 0.00309398 0.00074358]
 [0.00380489 0.00495984 0.00362077 ... 0.00459354 0.00307818 0.00075501]
 ...
 [0.00032765 0.00026166 0.00027507 ... 0.00532256 0.00767149 0.00406465]
 [0.00056232 0.00045415 0.00048093 ... 0.008659   0.01009767 0.00534725]
 [0.00674313 0.00620431 0.00772064 ... 0.24096705 0.00862882 0.00949841]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.9 [%]
Global accuracy score (test) = 40.51 [%]
Global F1 score (train) = 33.91 [%]
Global F1 score (test) = 31.45 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.94      0.36       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.48      0.65      0.55       161
   DE PIE DOBLANDO TOALLAS       0.38      0.41      0.39       161
    DE PIE MOVIENDO LIBROS       0.00      0.00      0.00       161
          DE PIE USANDO PC       0.53      0.86      0.66       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       0.98      0.89      0.94       161
           SENTADO LEYENDO       0.26      1.00      0.41       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.59      0.63      0.61       161
                    TROTAR       0.87      0.75      0.80       138

                  accuracy                           0.41      2392
                 macro avg       0.29      0.41      0.31      2392
              weighted avg       0.28      0.41      0.31      2392

2025-11-05 16:27:49.681822: 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 16:27:49.693102: 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 00:00:1762356469.706916 3679295 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356469.711018 3679295 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356469.720958 3679295 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356469.720977 3679295 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356469.720979 3679295 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356469.720981 3679295 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:27:49.724150: 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 00:00:1762356471.955863 3679295 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356473.392465 3679405 service.cc:152] XLA service 0x767e84009f10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356473.392533 3679405 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:27:53.433496: 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 00:00:1762356473.553748 3679405 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356475.787481 3679405 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:22[0m 3s/step - accuracy: 0.0469 - loss: 4.1216
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[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0818 - loss: 3.8979
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[1m158/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0910 - loss: 3.6977
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[1m238/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0970 - loss: 3.5452
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.0996 - loss: 3.4860
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.0997 - loss: 3.4845 - val_accuracy: 0.2520 - val_loss: 2.1189
Epoch 2/98

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[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1484 - loss: 2.4751
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[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1544 - loss: 2.4412
[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1565 - loss: 2.4262
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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2688 - loss: 1.9285
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Epoch 7/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2835 - loss: 1.8916
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[1m197/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2852 - loss: 1.8812
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2854 - loss: 1.8796 - val_accuracy: 0.3656 - val_loss: 1.5418
Epoch 8/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2990 - loss: 1.8670 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2993 - loss: 1.8567
[1m120/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2997 - loss: 1.8543
[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3002 - loss: 1.8529
[1m205/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2997 - loss: 1.8536
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Epoch 9/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3072 - loss: 1.8436 
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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3093 - loss: 1.7748
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[1m241/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3160 - loss: 1.7754
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3167 - loss: 1.7748 - val_accuracy: 0.3644 - val_loss: 1.5045
Epoch 14/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3368 - loss: 1.7012 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3349 - loss: 1.7242
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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.7479
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3322 - loss: 1.7504
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3319 - loss: 1.7515 - val_accuracy: 0.3672 - val_loss: 1.5359
Epoch 15/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3182 - loss: 1.7781 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3212 - loss: 1.7669
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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.6784
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[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.6873
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.6892
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3525 - loss: 1.6901 - val_accuracy: 0.4125 - val_loss: 1.4684
Epoch 21/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7124 
[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7132
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Epoch 22/98

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Epoch 23/98

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Epoch 24/98

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Epoch 25/98

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[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3595 - loss: 1.6620
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3594 - loss: 1.6631 - val_accuracy: 0.4030 - val_loss: 1.4412
Epoch 26/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3764 - loss: 1.6476 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3698 - loss: 1.6597
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[1m215/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.6681
[1m257/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3611 - loss: 1.6691
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3608 - loss: 1.6694 - val_accuracy: 0.4146 - val_loss: 1.4476
Epoch 27/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.6591 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.6636
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Epoch 28/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3596 - loss: 1.6660
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[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.6676
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3597 - loss: 1.6677
[1m247/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3590 - loss: 1.6681
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3587 - loss: 1.6683 - val_accuracy: 0.3856 - val_loss: 1.4607
Epoch 29/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3750 - loss: 1.8716
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7292 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7016
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.6902
[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.6836
[1m206/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.6784
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3538 - loss: 1.6756
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3540 - loss: 1.6745 - val_accuracy: 0.3739 - val_loss: 1.4510
Epoch 30/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3750 - loss: 1.8860
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3773 - loss: 1.6328 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.6342
[1m125/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3745 - loss: 1.6371
[1m169/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3730 - loss: 1.6390
[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3716 - loss: 1.6411
[1m254/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3707 - loss: 1.6425
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3702 - loss: 1.6433 - val_accuracy: 0.3680 - val_loss: 1.4754

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 714ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 715us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 5: 40.51 [%]
F1-score capturado en la ejecución 5: 31.45 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:21[0m 918ms/step
[1m 68/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 749us/step  
[1m137/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 741us/step
[1m214/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 710us/step
[1m289/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 700us/step
[1m362/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 697us/step
[1m432/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 700us/step
[1m502/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 703us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 699us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 77/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 661us/step
[1m147/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 687us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 36.8 [%]
Global F1 score (validation) = 30.27 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.3600016e-03 8.1747714e-03 5.9144911e-03 ... 2.4584371e-03
  6.5003317e-03 2.9160755e-04]
 [3.8194149e-03 3.9793858e-03 3.3112427e-03 ... 4.6399343e-03
  2.1315187e-03 3.2053213e-04]
 [3.5321272e-03 3.7196407e-03 2.9774685e-03 ... 3.9982265e-03
  2.3028015e-03 2.8659508e-04]
 ...
 [4.2405492e-04 4.8849836e-04 2.9417899e-04 ... 3.6191107e-03
  1.3578611e-02 3.6044305e-03]
 [8.3022809e-05 9.8241479e-05 5.1684525e-05 ... 5.8881735e-04
  5.9551592e-03 1.0315138e-03]
 [7.5959289e-03 8.3549647e-03 8.4530646e-03 ... 2.4442074e-01
  1.0464008e-02 8.3831977e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 42.17 [%]
Global accuracy score (test) = 38.75 [%]
Global F1 score (train) = 35.45 [%]
Global F1 score (test) = 31.34 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.14      0.24      0.17       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.25      0.68      0.37       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.44      0.71      0.55       161
   DE PIE DOBLANDO TOALLAS       0.00      0.00      0.00       161
    DE PIE MOVIENDO LIBROS       0.27      0.20      0.23       161
          DE PIE USANDO PC       0.49      0.86      0.63       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       0.99      0.88      0.93       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.26      0.99      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.55      0.58       161
                    TROTAR       0.94      0.74      0.83       138

                  accuracy                           0.39      2392
                 macro avg       0.29      0.39      0.31      2392
              weighted avg       0.29      0.39      0.31      2392

2025-11-05 16:28:25.910569: 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 16:28:25.922005: 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 00:00:1762356505.935808 3683085 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356505.939777 3683085 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356505.949709 3683085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356505.949729 3683085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356505.949731 3683085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356505.949732 3683085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:28:25.952896: 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 00:00:1762356508.240506 3683085 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356509.665436 3683214 service.cc:152] XLA service 0x716fd4004740 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356509.665465 3683214 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:28:29.698877: 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 00:00:1762356509.822793 3683214 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356512.076429 3683214 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3004 - loss: 1.8435
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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3322 - loss: 1.7323
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Epoch 16/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.7000
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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.6676
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[1m241/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.6756
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3474 - loss: 1.6771 - val_accuracy: 0.3826 - val_loss: 1.4644
Epoch 21/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3717 - loss: 1.6434 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3589 - loss: 1.6611
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[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3540 - loss: 1.6720
[1m205/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3535 - loss: 1.6732
[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.6743
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3533 - loss: 1.6748 - val_accuracy: 0.3860 - val_loss: 1.4565
Epoch 22/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3548 - loss: 1.6514 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3550 - loss: 1.6569
[1m122/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.6613
[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.6639
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.6645
[1m244/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3550 - loss: 1.6658
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3546 - loss: 1.6670 - val_accuracy: 0.3804 - val_loss: 1.4749
Epoch 23/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.6535 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.6648
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[1m242/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.6729
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Epoch 24/98

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Epoch 25/98

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Epoch 26/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3125 - loss: 1.6823
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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3648 - loss: 1.6569
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[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3633 - loss: 1.6559
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Epoch 27/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8767
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3481 - loss: 1.6987 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.6777
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[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.6674
[1m252/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.6670
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3536 - loss: 1.6666 - val_accuracy: 0.4142 - val_loss: 1.4507
Epoch 28/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.3438 - loss: 1.9471
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3678 - loss: 1.7054 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3658 - loss: 1.6919
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[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.6791
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3578 - loss: 1.6768
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3574 - loss: 1.6757 - val_accuracy: 0.4225 - val_loss: 1.4554
Epoch 29/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3609 - loss: 1.6648 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.6534
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Epoch 30/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.4375 - loss: 1.5158
[1m 36/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3662 - loss: 1.6443 
[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.6592
[1m114/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.6586
[1m156/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.6576
[1m199/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.6574
[1m242/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3566 - loss: 1.6569
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3570 - loss: 1.6563 - val_accuracy: 0.4071 - val_loss: 1.4705
Epoch 31/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.4375 - loss: 1.4545
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3657 - loss: 1.6026 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3641 - loss: 1.6140
[1m124/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.6184
[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3671 - loss: 1.6194
[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3682 - loss: 1.6206
[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3686 - loss: 1.6219
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3688 - loss: 1.6229 - val_accuracy: 0.4103 - val_loss: 1.4598
Epoch 32/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6564
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3876 - loss: 1.6248 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3806 - loss: 1.6260
[1m127/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3773 - loss: 1.6266
[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3760 - loss: 1.6285
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3746 - loss: 1.6296
[1m254/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3733 - loss: 1.6313
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3728 - loss: 1.6320 - val_accuracy: 0.4326 - val_loss: 1.4540

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 708ms/step
[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 871us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 6: 38.75 [%]
F1-score capturado en la ejecución 6: 31.34 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:14[0m 906ms/step
[1m 66/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 770us/step  
[1m133/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 762us/step
[1m206/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 736us/step
[1m276/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 731us/step
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[1m413/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 732us/step
[1m483/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 729us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 751us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 67/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 761us/step
[1m139/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 726us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 43.26 [%]
Global F1 score (validation) = 39.0 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.0056086  0.00741809 0.00599445 ... 0.00257176 0.0078482  0.00030291]
 [0.00315989 0.0040866  0.00339194 ... 0.00293309 0.00300766 0.0002983 ]
 [0.00285407 0.00364931 0.00305062 ... 0.00309122 0.00265678 0.00032027]
 ...
 [0.00054607 0.00054523 0.00051801 ... 0.01731563 0.007257   0.00985113]
 [0.00035566 0.00034797 0.00033225 ... 0.00984226 0.00618838 0.00752948]
 [0.00420149 0.00409657 0.00369238 ... 0.27898198 0.00568225 0.00517674]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 47.0 [%]
Global accuracy score (test) = 45.44 [%]
Global F1 score (train) = 42.07 [%]
Global F1 score (test) = 40.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.27      0.17      0.21       161
       CAMINAR USUAL SPEED       0.26      0.92      0.40       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.48      0.65      0.55       161
   DE PIE DOBLANDO TOALLAS       0.32      0.30      0.31       161
    DE PIE MOVIENDO LIBROS       0.40      0.22      0.28       161
          DE PIE USANDO PC       0.72      0.85      0.78       161
        FASE REPOSO CON K5       0.52      0.86      0.65       161
INCREMENTAL CICLOERGOMETRO       0.99      0.89      0.94       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.18      0.13      0.15       161
      SENTADO VIENDO LA TV       0.29      0.44      0.35       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.66      0.62       161
                    TROTAR       0.93      0.77      0.84       138

                  accuracy                           0.45      2392
                 macro avg       0.40      0.46      0.41      2392
              weighted avg       0.39      0.45      0.40      2392

2025-11-05 16:29:03.319369: 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 16:29:03.330725: 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 00:00:1762356543.344006 3687059 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356543.348162 3687059 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356543.358336 3687059 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356543.358357 3687059 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356543.358360 3687059 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356543.358361 3687059 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:29:03.361668: 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 00:00:1762356545.630010 3687059 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356547.068998 3687192 service.cc:152] XLA service 0x78c0a400b440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356547.069036 3687192 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:29:07.103279: 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 00:00:1762356547.225094 3687192 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356549.479053 3687192 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.7365 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3353 - loss: 1.7321
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[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3319 - loss: 1.7363
[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.7354
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3320 - loss: 1.7351 - val_accuracy: 0.3802 - val_loss: 1.4688
Epoch 16/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7075 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.7073
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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.6790
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[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.6927
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Epoch 21/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3661 - loss: 1.6802 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.6861
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[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.6851
[1m240/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3572 - loss: 1.6851
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3568 - loss: 1.6852 - val_accuracy: 0.3984 - val_loss: 1.4672
Epoch 22/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.6970 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3502 - loss: 1.6935
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Epoch 23/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3673 - loss: 1.6524 
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[1m247/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3557 - loss: 1.6839
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3551 - loss: 1.6843 - val_accuracy: 0.3964 - val_loss: 1.4585
Epoch 24/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8894
[1m 36/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3388 - loss: 1.7492 
[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.7250
[1m113/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.7104
[1m152/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7007
[1m193/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3553 - loss: 1.6945
[1m240/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.6905
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3560 - loss: 1.6888 - val_accuracy: 0.3751 - val_loss: 1.4568
Epoch 25/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3750 - loss: 1.6263
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.6845 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.6790
[1m125/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.6744
[1m170/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.6723
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.6733
[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.6739
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3493 - loss: 1.6743 - val_accuracy: 0.3822 - val_loss: 1.4582

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 716ms/step
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 797us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 7: 45.44 [%]
F1-score capturado en la ejecución 7: 40.56 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:01[0m 882ms/step
[1m 62/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 825us/step  
[1m120/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 847us/step
[1m192/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 792us/step
[1m268/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 756us/step
[1m339/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 745us/step
[1m414/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 732us/step
[1m488/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 725us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 759us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 66/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 778us/step
[1m139/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 732us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 38.22 [%]
Global F1 score (validation) = 32.19 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.0030970e-03 5.7756859e-03 4.5946897e-03 ... 3.2325673e-03
  1.1886161e-02 3.5230618e-04]
 [3.8235090e-03 4.3049320e-03 3.9500813e-03 ... 3.7612598e-03
  4.1157785e-03 3.5702740e-04]
 [3.7146055e-03 4.1548833e-03 3.9517623e-03 ... 4.1839322e-03
  3.2585317e-03 3.6707564e-04]
 ...
 [5.8030133e-04 5.5303576e-04 5.9972238e-04 ... 1.6912503e-02
  5.9477054e-03 6.8587023e-03]
 [2.3915755e-04 2.2160537e-04 2.3677059e-04 ... 4.9575870e-03
  3.3497300e-03 5.6707091e-03]
 [7.0487619e-03 7.0381686e-03 8.7501500e-03 ... 2.5355887e-01
  7.7460622e-03 8.5260887e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 43.37 [%]
Global accuracy score (test) = 41.81 [%]
Global F1 score (train) = 37.26 [%]
Global F1 score (test) = 35.36 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.67      0.37       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.21      0.29      0.24       161
          DE PIE BARRIENDO       0.51      0.61      0.55       161
   DE PIE DOBLANDO TOALLAS       0.35      0.57      0.43       161
    DE PIE MOVIENDO LIBROS       0.00      0.00      0.00       161
          DE PIE USANDO PC       0.67      0.88      0.76       161
        FASE REPOSO CON K5       0.27      0.87      0.41       161
INCREMENTAL CICLOERGOMETRO       1.00      0.84      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.27      0.20      0.23       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.63      0.58       161
                    TROTAR       0.83      0.78      0.81       138

                  accuracy                           0.42      2392
                 macro avg       0.33      0.42      0.35      2392
              weighted avg       0.32      0.42      0.35      2392

2025-11-05 16:29:37.322103: 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 16:29:37.333454: 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 00:00:1762356577.346575 3690404 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356577.350661 3690404 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356577.360529 3690404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356577.360549 3690404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356577.360552 3690404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356577.360563 3690404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:29:37.363738: 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 00:00:1762356579.665047 3690404 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356581.072620 3690544 service.cc:152] XLA service 0x70558800a840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356581.072659 3690544 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:29:41.109902: 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 00:00:1762356581.229355 3690544 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356583.478828 3690544 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3044 - loss: 1.7941
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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.7317
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3338 - loss: 1.7345 - val_accuracy: 0.3690 - val_loss: 1.4980
Epoch 15/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.4531 - loss: 1.6085
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3310 - loss: 1.7418 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 1.7376
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[1m170/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3304 - loss: 1.7322
[1m213/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3309 - loss: 1.7325
[1m255/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3311 - loss: 1.7329
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3314 - loss: 1.7327 - val_accuracy: 0.3899 - val_loss: 1.4961
Epoch 16/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.8032
[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.7319 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7282
[1m119/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7264
[1m159/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7259
[1m200/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.7259
[1m241/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3393 - loss: 1.7252
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3393 - loss: 1.7248 - val_accuracy: 0.3840 - val_loss: 1.4932
Epoch 17/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3232 - loss: 1.7528 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3272 - loss: 1.7460
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Epoch 18/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2969 - loss: 1.9305
[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7561 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7238
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[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7086
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7069
[1m240/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7059
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Epoch 19/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 29ms/step - accuracy: 0.2969 - loss: 1.8249
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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.6866
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3453 - loss: 1.6866 - val_accuracy: 0.3929 - val_loss: 1.4803
Epoch 20/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.3281 - loss: 1.5549
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.6329 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.6512
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3453 - loss: 1.6586
[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.6633
[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.6677
[1m252/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.6708
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3457 - loss: 1.6720 - val_accuracy: 0.3883 - val_loss: 1.4832
Epoch 21/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3750 - loss: 1.8270
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.6601 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.6628
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.6655
[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.6670
[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.6690
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3494 - loss: 1.6718
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3493 - loss: 1.6733 - val_accuracy: 0.3781 - val_loss: 1.4808
Epoch 22/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3750 - loss: 1.5287
[1m 34/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3844 - loss: 1.6402 
[1m 75/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3712 - loss: 1.6535
[1m118/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3640 - loss: 1.6635
[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.6683
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3592 - loss: 1.6696
[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.6704
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3576 - loss: 1.6707 - val_accuracy: 0.3737 - val_loss: 1.4817

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 718ms/step
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 738us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 8: 41.81 [%]
F1-score capturado en la ejecución 8: 35.36 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:09[0m 897ms/step
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[1m134/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 757us/step
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 711us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 68/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 755us/step
[1m145/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 700us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.37 [%]
Global F1 score (validation) = 32.4 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.6970236e-03 8.9438427e-03 5.7318970e-03 ... 2.6478556e-03
  1.0942371e-02 3.8788651e-04]
 [3.1986081e-03 4.8308969e-03 3.1815376e-03 ... 4.3335836e-03
  5.5250186e-03 5.0389336e-04]
 [2.7285437e-03 4.0983991e-03 2.7080784e-03 ... 4.2894795e-03
  4.4586388e-03 4.7185156e-04]
 ...
 [8.9793821e-04 8.1580854e-04 6.5550749e-04 ... 1.2310680e-02
  8.6726099e-03 1.0878060e-02]
 [1.4940111e-04 1.3435671e-04 9.8982491e-05 ... 1.4883149e-03
  3.1000676e-03 3.6767656e-03]
 [8.4614176e-03 8.2592359e-03 8.4299184e-03 ... 1.9613379e-01
  8.6937901e-03 6.8540685e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.55 [%]
Global accuracy score (test) = 40.38 [%]
Global F1 score (train) = 36.2 [%]
Global F1 score (test) = 35.96 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.11      0.02      0.03       161
       CAMINAR USUAL SPEED       0.25      0.91      0.39       161
            CAMINAR ZIGZAG       0.33      0.09      0.14       161
          DE PIE BARRIENDO       0.48      0.62      0.54       161
   DE PIE DOBLANDO TOALLAS       0.30      0.34      0.32       161
    DE PIE MOVIENDO LIBROS       0.27      0.39      0.32       161
          DE PIE USANDO PC       0.82      0.19      0.31       161
        FASE REPOSO CON K5       0.33      0.86      0.48       161
INCREMENTAL CICLOERGOMETRO       0.99      0.88      0.93       161
           SENTADO LEYENDO       0.88      0.14      0.25       161
         SENTADO USANDO PC       0.27      0.31      0.29       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.61      0.57       161
                    TROTAR       0.92      0.75      0.82       138

                  accuracy                           0.40      2392
                 macro avg       0.43      0.41      0.36      2392
              weighted avg       0.43      0.40      0.36      2392

2025-11-05 16:30:09.775614: 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 16:30:09.786983: 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 00:00:1762356609.800223 3693448 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356609.804412 3693448 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356609.814223 3693448 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356609.814241 3693448 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356609.814243 3693448 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356609.814245 3693448 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:30:09.817398: 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 00:00:1762356612.043220 3693448 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356613.484759 3693585 service.cc:152] XLA service 0x7df9ec0045f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356613.484820 3693585 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:30:13.524746: 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 00:00:1762356613.648125 3693585 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356615.885597 3693585 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:27[0m 3s/step - accuracy: 0.0781 - loss: 4.4602
[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0772 - loss: 4.1156  
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0866 - loss: 3.9184
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0921 - loss: 3.7806
[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0962 - loss: 3.6657
[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0996 - loss: 3.5779
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1030 - loss: 3.5005
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.1048 - loss: 3.4593
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.1049 - loss: 3.4577 - val_accuracy: 0.2538 - val_loss: 2.1263
Epoch 2/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1641 - loss: 2.4770
[1m124/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1668 - loss: 2.4620
[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1697 - loss: 2.4457
[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1725 - loss: 2.4302
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1751 - loss: 2.4151
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.1766 - loss: 2.4061 - val_accuracy: 0.3184 - val_loss: 1.7959
Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2715 - loss: 1.9052 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2782 - loss: 1.8940
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2827 - loss: 1.8916 - val_accuracy: 0.3526 - val_loss: 1.5568
Epoch 7/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2740 - loss: 1.8816 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2803 - loss: 1.8713
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[1m214/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2872 - loss: 1.8576
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2893 - loss: 1.8557 - val_accuracy: 0.3494 - val_loss: 1.5578
Epoch 8/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3007 - loss: 1.8846 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3029 - loss: 1.8650
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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3169 - loss: 1.7783
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Epoch 13/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3290 - loss: 1.7306 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3268 - loss: 1.7445
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[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3282 - loss: 1.7490
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3295 - loss: 1.7503 - val_accuracy: 0.3812 - val_loss: 1.4845
Epoch 14/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.7724 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3225 - loss: 1.7606
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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3336 - loss: 1.7206
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[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7131
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Epoch 20/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.6743 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.6875
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Epoch 21/98

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Epoch 22/98

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Epoch 23/98

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Epoch 24/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.6987
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3490 - loss: 1.6878 - val_accuracy: 0.3941 - val_loss: 1.4777
Epoch 25/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.7044 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.6923
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[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.6826
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3525 - loss: 1.6817 - val_accuracy: 0.3743 - val_loss: 1.4589
Epoch 26/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3249 - loss: 1.7286 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3343 - loss: 1.7028
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Epoch 27/98

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[1m150/274[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3563 - loss: 1.6538
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[1m235/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3578 - loss: 1.6562
[1m272/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3583 - loss: 1.6569
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3583 - loss: 1.6570 - val_accuracy: 0.4073 - val_loss: 1.4668

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 776ms/step
[1m62/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 828us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 9: 40.38 [%]
F1-score capturado en la ejecución 9: 35.96 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:54[0m 869ms/step
[1m 64/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 811us/step  
[1m138/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 741us/step
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[1m279/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 727us/step
[1m357/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 708us/step
[1m425/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 713us/step
[1m496/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 712us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 710us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 70/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 727us/step
[1m140/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 723us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 40.73 [%]
Global F1 score (validation) = 35.56 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[3.4400169e-03 5.4915464e-03 2.8331333e-03 ... 2.4741988e-03
  9.3305903e-03 1.8143909e-04]
 [2.5908630e-03 4.1646329e-03 2.5456604e-03 ... 4.9420027e-03
  4.5822975e-03 2.8633390e-04]
 [2.5012803e-03 4.0137852e-03 2.5423060e-03 ... 5.7164207e-03
  4.1732048e-03 3.3170320e-04]
 ...
 [4.3983496e-04 4.6574624e-04 3.9026403e-04 ... 8.8329306e-03
  1.0754998e-02 8.5605327e-03]
 [9.1050152e-04 1.0026851e-03 8.9562923e-04 ... 2.5741205e-02
  1.2543551e-02 9.1336099e-03]
 [5.9374329e-03 7.1241958e-03 8.3462317e-03 ... 2.5980279e-01
  8.2769571e-03 7.9840245e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 44.7 [%]
Global accuracy score (test) = 44.82 [%]
Global F1 score (train) = 40.11 [%]
Global F1 score (test) = 39.72 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.85      0.33       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.46      0.70      0.56       161
   DE PIE DOBLANDO TOALLAS       0.33      0.18      0.23       161
    DE PIE MOVIENDO LIBROS       0.28      0.20      0.23       161
          DE PIE USANDO PC       0.65      0.86      0.74       161
        FASE REPOSO CON K5       0.69      0.71      0.70       161
INCREMENTAL CICLOERGOMETRO       0.99      0.89      0.94       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.37      0.65      0.47       161
      SENTADO VIENDO LA TV       0.31      0.35      0.33       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.59      0.58       161
                    TROTAR       0.88      0.79      0.83       138

                  accuracy                           0.45      2392
                 macro avg       0.38      0.45      0.40      2392
              weighted avg       0.38      0.45      0.39      2392

2025-11-05 16:30:44.494550: 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 16:30:44.505814: 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 00:00:1762356644.518802 3696970 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356644.523012 3696970 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356644.532882 3696970 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356644.532901 3696970 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356644.532904 3696970 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356644.532905 3696970 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:30:44.536032: 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 00:00:1762356646.787080 3696970 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356648.207658 3697102 service.cc:152] XLA service 0x7a311400aa60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356648.207724 3697102 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:30:48.244304: 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 00:00:1762356648.362289 3697102 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356650.592782 3697102 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:15[0m 3s/step - accuracy: 0.0625 - loss: 3.9830
[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0816 - loss: 3.9270  
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[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0920 - loss: 3.6743
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Epoch 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 1.9627
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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3153 - loss: 1.7966
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[1m206/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3170 - loss: 1.7986
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3171 - loss: 1.7967 - val_accuracy: 0.3808 - val_loss: 1.4949
Epoch 12/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3018 - loss: 1.7849 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.7809
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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.6792 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.6952
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[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.7060
[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7076
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Epoch 18/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3360 - loss: 1.6998 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7026
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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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[1m257/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.6873
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Epoch 22/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.6974 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.6834
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[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.6799
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.6798
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3479 - loss: 1.6800 - val_accuracy: 0.4016 - val_loss: 1.4492
Epoch 23/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.6755 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.6647
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[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.6620
[1m206/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.6629
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.6637
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3524 - loss: 1.6640 - val_accuracy: 0.3854 - val_loss: 1.4608
Epoch 24/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.6657 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.6612
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3678 - loss: 1.6633
[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.6651
[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3631 - loss: 1.6659
[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.6670
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3609 - loss: 1.6676 - val_accuracy: 0.3921 - val_loss: 1.4502
Epoch 25/98

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[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3754 - loss: 1.6627
[1m244/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3741 - loss: 1.6629
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3731 - loss: 1.6632 - val_accuracy: 0.3966 - val_loss: 1.4636
Epoch 26/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.7549
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.6772 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.6742
[1m124/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.6757
[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.6730
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3507 - loss: 1.6703
[1m244/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.6689
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3518 - loss: 1.6682 - val_accuracy: 0.3868 - val_loss: 1.4799
Epoch 27/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3750 - loss: 1.6832
[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3678 - loss: 1.6468 
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[1m127/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3628 - loss: 1.6545
[1m168/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.6541
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.6545
[1m255/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.6558
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3613 - loss: 1.6563 - val_accuracy: 0.4101 - val_loss: 1.4712

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 698ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 789us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 10: 44.82 [%]
F1-score capturado en la ejecución 10: 39.72 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:04[0m 887ms/step
[1m 67/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 764us/step  
[1m134/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 755us/step
[1m200/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 758us/step
[1m272/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 745us/step
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[1m428/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 708us/step
[1m493/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 718us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 710us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 740us/step
[1m139/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 728us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 41.01 [%]
Global F1 score (validation) = 36.17 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.5168229e-03 7.7494336e-03 4.4184402e-03 ... 2.7920408e-03
  1.5795980e-02 4.5547399e-04]
 [2.4333254e-03 3.5250138e-03 2.4288946e-03 ... 3.1112114e-03
  3.4937032e-03 2.3583655e-04]
 [2.8445993e-03 4.1738837e-03 2.6431922e-03 ... 2.6762029e-03
  4.6502352e-03 2.3652842e-04]
 ...
 [6.4298569e-04 6.3388888e-04 6.9196173e-04 ... 1.6979583e-02
  5.7690954e-03 1.5794219e-02]
 [2.0020119e-04 1.9234532e-04 2.2099681e-04 ... 5.7906029e-03
  2.0425206e-03 1.7066306e-02]
 [5.9617329e-03 5.9467559e-03 8.1626335e-03 ... 2.3298997e-01
  6.1618155e-03 7.5573502e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 45.06 [%]
Global accuracy score (test) = 45.07 [%]
Global F1 score (train) = 40.67 [%]
Global F1 score (test) = 41.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.81      0.36       161
 CAMINAR CON MÓVIL O LIBRO       0.28      0.19      0.23       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.14      0.01      0.01       161
          DE PIE BARRIENDO       0.48      0.39      0.43       161
   DE PIE DOBLANDO TOALLAS       0.35      0.60      0.44       161
    DE PIE MOVIENDO LIBROS       0.62      0.09      0.16       161
          DE PIE USANDO PC       0.67      0.91      0.77       161
        FASE REPOSO CON K5       0.91      0.61      0.73       161
INCREMENTAL CICLOERGOMETRO       0.98      0.84      0.90       161
           SENTADO LEYENDO       0.42      0.14      0.21       161
         SENTADO USANDO PC       0.27      0.75      0.39       161
      SENTADO VIENDO LA TV       0.78      0.11      0.20       161
   SUBIR Y BAJAR ESCALERAS       0.57      0.63      0.60       161
                    TROTAR       0.89      0.72      0.80       138

                  accuracy                           0.45      2392
                 macro avg       0.51      0.45      0.42      2392
              weighted avg       0.50      0.45      0.41      2392

2025-11-05 16:31:19.066833: 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 16:31:19.078059: 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 00:00:1762356679.091281 3700488 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356679.095446 3700488 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356679.105327 3700488 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356679.105347 3700488 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356679.105349 3700488 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356679.105350 3700488 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:31:19.108703: 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 00:00:1762356681.390540 3700488 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356682.846664 3700621 service.cc:152] XLA service 0x7ca98001b1c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356682.846696 3700621 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:31:22.880599: 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 00:00:1762356683.005972 3700621 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356685.261163 3700621 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.7820
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Epoch 10/98

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[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3016 - loss: 1.8342
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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3243 - loss: 1.7762 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3303 - loss: 1.7581
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[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3373 - loss: 1.7361
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3389 - loss: 1.7321 - val_accuracy: 0.3688 - val_loss: 1.4689
Epoch 16/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.7758 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3268 - loss: 1.7635
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Epoch 17/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.6965 
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Epoch 18/98

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[1m 89/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7044
[1m129/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.7009
[1m171/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.6983
[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.6969
[1m252/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.6963
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3480 - loss: 1.6965 - val_accuracy: 0.3858 - val_loss: 1.4529
Epoch 19/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.4219 - loss: 1.7438
[1m 36/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3697 - loss: 1.6991 
[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.7023
[1m119/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.7044
[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7020
[1m205/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7008
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.7000
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3487 - loss: 1.6997 - val_accuracy: 0.3798 - val_loss: 1.4677
Epoch 20/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.4062 - loss: 1.7308
[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3661 - loss: 1.6860 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3643 - loss: 1.6812
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3626 - loss: 1.6809
[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.6811
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3596 - loss: 1.6817
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3590 - loss: 1.6823
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3583 - loss: 1.6826 - val_accuracy: 0.3879 - val_loss: 1.4590
Epoch 21/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3594 - loss: 1.4892
[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.6958 
[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.7027
[1m120/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3442 - loss: 1.7025
[1m162/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7012
[1m205/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7001
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.6988
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3464 - loss: 1.6977 - val_accuracy: 0.3862 - val_loss: 1.4631
Epoch 22/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3594 - loss: 1.7087
[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3481 - loss: 1.7309 
[1m 75/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7179
[1m116/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7099
[1m158/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7065
[1m201/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7030
[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7000
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3474 - loss: 1.6976 - val_accuracy: 0.3759 - val_loss: 1.4637
Epoch 23/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3281 - loss: 1.5484
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3349 - loss: 1.6869 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.6855
[1m123/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.6839
[1m162/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3442 - loss: 1.6832
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.6820
[1m244/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.6806
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.6800 - val_accuracy: 0.3828 - val_loss: 1.4761

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 739ms/step
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 809us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 11: 45.07 [%]
F1-score capturado en la ejecución 11: 41.56 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:08[0m 895ms/step
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[1m146/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 696us/step
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 707us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 67/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 761us/step
[1m136/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 745us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 38.28 [%]
Global F1 score (validation) = 31.01 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[2.6811445e-03 4.5013358e-03 2.7018471e-03 ... 3.2413695e-03
  5.4916125e-03 3.1919446e-04]
 [2.5061979e-03 4.1751545e-03 2.6614957e-03 ... 4.0550297e-03
  3.0260284e-03 3.7340313e-04]
 [3.5806678e-03 5.8566686e-03 3.7713302e-03 ... 4.8514009e-03
  4.2702993e-03 5.2831700e-04]
 ...
 [2.0912083e-04 1.6490035e-04 1.6835266e-04 ... 3.4989419e-03
  3.8879451e-03 1.0417284e-02]
 [1.8738852e-04 1.4275074e-04 1.4567385e-04 ... 2.9117870e-03
  3.7681970e-03 1.0792726e-02]
 [6.9306418e-03 6.4677135e-03 8.9572491e-03 ... 2.2034732e-01
  4.3541165e-03 5.7575498e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.75 [%]
Global accuracy score (test) = 40.47 [%]
Global F1 score (train) = 34.06 [%]
Global F1 score (test) = 33.9 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.05      0.01      0.01       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.22      0.89      0.36       161
          DE PIE BARRIENDO       0.45      0.57      0.51       161
   DE PIE DOBLANDO TOALLAS       0.00      0.00      0.00       161
    DE PIE MOVIENDO LIBROS       0.27      0.45      0.33       161
          DE PIE USANDO PC       0.79      0.86      0.82       161
        FASE REPOSO CON K5       0.25      0.89      0.39       161
INCREMENTAL CICLOERGOMETRO       0.99      0.84      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       1.00      0.14      0.25       161
      SENTADO VIENDO LA TV       0.14      0.03      0.05       161
   SUBIR Y BAJAR ESCALERAS       0.57      0.65      0.61       161
                    TROTAR       0.92      0.80      0.85       138

                  accuracy                           0.40      2392
                 macro avg       0.38      0.41      0.34      2392
              weighted avg       0.37      0.40      0.33      2392

2025-11-05 16:31:51.903690: 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 16:31:51.915236: 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 00:00:1762356711.928929 3703635 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356711.932929 3703635 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356711.943044 3703635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356711.943066 3703635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356711.943068 3703635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356711.943069 3703635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:31:51.946410: 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 00:00:1762356714.203771 3703635 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356715.638373 3703747 service.cc:152] XLA service 0x76a42401cdc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356715.638408 3703747 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:31:55.671742: 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 00:00:1762356715.795647 3703747 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356718.021117 3703747 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:18[0m 3s/step - accuracy: 0.0156 - loss: 4.1849
[1m 36/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0676 - loss: 4.0403  
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0800 - loss: 3.8611
[1m120/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0858 - loss: 3.7431
[1m162/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0897 - loss: 3.6439
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0928 - loss: 3.5604
[1m240/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0955 - loss: 3.4931
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.0980 - loss: 3.4375
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.0981 - loss: 3.4359 - val_accuracy: 0.2462 - val_loss: 2.0904
Epoch 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.8943
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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2976 - loss: 1.7728 
[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3051 - loss: 1.7694
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3150 - loss: 1.7677 - val_accuracy: 0.3775 - val_loss: 1.4748
Epoch 13/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7279 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3311 - loss: 1.7438
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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 89/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7420
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3376 - loss: 1.7299 - val_accuracy: 0.3626 - val_loss: 1.4903
Epoch 18/98

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[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.7284 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.7090
[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3602 - loss: 1.7020
[1m159/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.7014
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.7019
[1m242/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.7015
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3544 - loss: 1.7020 - val_accuracy: 0.4109 - val_loss: 1.4598
Epoch 19/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.6906 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.7007
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[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7108
[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7116
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Epoch 20/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.6637 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.6741
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Epoch 21/98

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Epoch 22/98

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Epoch 23/98

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[1m247/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.6801
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3546 - loss: 1.6803 - val_accuracy: 0.3694 - val_loss: 1.4849
Epoch 24/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3716 - loss: 1.6708 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3708 - loss: 1.6722
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[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3649 - loss: 1.6745
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3635 - loss: 1.6750
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.6768
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3605 - loss: 1.6780 - val_accuracy: 0.4125 - val_loss: 1.4516
Epoch 25/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.6389 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.6541
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[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3487 - loss: 1.6624
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.6641
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.6645
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3502 - loss: 1.6644 - val_accuracy: 0.3947 - val_loss: 1.4550
Epoch 26/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.6404 
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[1m215/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.6568
[1m258/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.6588
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3557 - loss: 1.6592 - val_accuracy: 0.3899 - val_loss: 1.4657

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 727ms/step
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 752us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 12: 40.47 [%]
F1-score capturado en la ejecución 12: 33.9 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:07[0m 893ms/step
[1m 70/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 733us/step  
[1m144/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 704us/step
[1m217/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 697us/step
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[1m362/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 698us/step
[1m437/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 694us/step
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[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 703us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 66/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 773us/step
[1m138/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 736us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 38.99 [%]
Global F1 score (validation) = 33.88 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.9875062e-03 5.5153989e-03 4.2588944e-03 ... 3.5162896e-03
  4.9115191e-03 3.9805946e-04]
 [3.5830166e-03 4.2341999e-03 3.5283354e-03 ... 5.6396723e-03
  2.7020145e-03 5.7625194e-04]
 [3.6924104e-03 4.3792566e-03 3.6943355e-03 ... 6.6498420e-03
  2.9221573e-03 7.4758660e-04]
 ...
 [1.6512038e-04 1.4400532e-04 9.7497985e-05 ... 3.4394276e-03
  7.1327654e-03 2.5681288e-03]
 [1.5696581e-04 1.2531376e-04 8.5313332e-05 ... 2.8646237e-03
  6.3468241e-03 1.4415145e-03]
 [5.3732162e-03 6.4607104e-03 5.7647405e-03 ... 2.3789191e-01
  7.7077853e-03 3.9676148e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 44.17 [%]
Global accuracy score (test) = 41.18 [%]
Global F1 score (train) = 38.87 [%]
Global F1 score (test) = 35.65 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.16      0.42      0.23       161
 CAMINAR CON MÓVIL O LIBRO       0.12      0.01      0.01       161
       CAMINAR USUAL SPEED       0.36      0.15      0.21       161
            CAMINAR ZIGZAG       0.19      0.23      0.21       161
          DE PIE BARRIENDO       0.48      0.81      0.60       161
   DE PIE DOBLANDO TOALLAS       0.39      0.17      0.23       161
    DE PIE MOVIENDO LIBROS       0.00      0.00      0.00       161
          DE PIE USANDO PC       0.45      0.88      0.59       161
        FASE REPOSO CON K5       0.34      0.86      0.49       161
INCREMENTAL CICLOERGOMETRO       0.99      0.83      0.91       161
           SENTADO LEYENDO       0.42      0.56      0.48       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.55      0.56       161
                    TROTAR       0.91      0.76      0.83       138

                  accuracy                           0.41      2392
                 macro avg       0.36      0.42      0.36      2392
              weighted avg       0.35      0.41      0.35      2392

2025-11-05 16:32:26.255852: 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 16:32:26.266948: 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 00:00:1762356746.279862 3707037 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356746.283770 3707037 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356746.293708 3707037 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356746.293726 3707037 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356746.293728 3707037 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356746.293730 3707037 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:32:26.296845: 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 00:00:1762356748.547621 3707037 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356749.957447 3707177 service.cc:152] XLA service 0x772e9001ae80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356749.957481 3707177 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:32:29.990777: 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 00:00:1762356750.109022 3707177 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356752.346856 3707177 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:14[0m 3s/step - accuracy: 0.0469 - loss: 3.8960
[1m 35/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0768 - loss: 3.9121  
[1m 76/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0871 - loss: 3.7915
[1m113/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0919 - loss: 3.6934
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Epoch 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2791 - loss: 1.9191
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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.7850
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[1m170/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3149 - loss: 1.7859
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3152 - loss: 1.7845
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3160 - loss: 1.7831 - val_accuracy: 0.3557 - val_loss: 1.5035
Epoch 12/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3257 - loss: 1.7790 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.7738
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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7299
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[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7175
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Epoch 18/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3335 - loss: 1.7512 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7333
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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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[1m247/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.6989
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Epoch 22/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.6961 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.6930
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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.6953
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.6945
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3502 - loss: 1.6937 - val_accuracy: 0.3737 - val_loss: 1.4626
Epoch 23/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2812 - loss: 1.7871
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.6752 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.6620
[1m119/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.6600
[1m159/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.6606
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.6629
[1m241/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.6643
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3514 - loss: 1.6663 - val_accuracy: 0.3794 - val_loss: 1.4604
Epoch 24/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2969 - loss: 1.7900
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3412 - loss: 1.6729 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3450 - loss: 1.6682
[1m123/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.6674
[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3475 - loss: 1.6700
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.6733
[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.6756
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3480 - loss: 1.6765 - val_accuracy: 0.3933 - val_loss: 1.4720
Epoch 25/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3347 - loss: 1.6882 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.6817
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3475 - loss: 1.6781 - val_accuracy: 0.3870 - val_loss: 1.4738
Epoch 26/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.8385
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.6825 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.6803
[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.6782
[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.6776
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.6761
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.6746
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3521 - loss: 1.6739 - val_accuracy: 0.3599 - val_loss: 1.4870
Epoch 27/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2812 - loss: 1.5752
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.6586 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.6555
[1m123/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.6557
[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.6557
[1m206/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3626 - loss: 1.6556
[1m240/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.6557
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3614 - loss: 1.6561 - val_accuracy: 0.3921 - val_loss: 1.4608
Epoch 28/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 22ms/step - accuracy: 0.4219 - loss: 1.5578
[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3593 - loss: 1.7089 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3607 - loss: 1.6926
[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.6805
[1m169/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3604 - loss: 1.6725
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3597 - loss: 1.6697
[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3592 - loss: 1.6683
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3587 - loss: 1.6678 - val_accuracy: 0.3731 - val_loss: 1.4750

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 716ms/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 841us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 13: 41.18 [%]
F1-score capturado en la ejecución 13: 35.65 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:05[0m 889ms/step
[1m 65/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 789us/step  
[1m137/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 744us/step
[1m207/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 734us/step
[1m278/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 726us/step
[1m347/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 727us/step
[1m419/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 722us/step
[1m491/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 719us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 810us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 66/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 777us/step
[1m136/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 747us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.31 [%]
Global F1 score (validation) = 29.95 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.00293511 0.00414827 0.00306611 ... 0.00189632 0.00890327 0.00029333]
 [0.0024203  0.00359139 0.00283971 ... 0.00366307 0.00342538 0.0004209 ]
 [0.00249742 0.00371736 0.00295361 ... 0.00403424 0.00329958 0.00045748]
 ...
 [0.00081048 0.00081377 0.00079477 ... 0.01387867 0.00559724 0.0051261 ]
 [0.00030768 0.00029094 0.00027053 ... 0.00277687 0.00286937 0.00128323]
 [0.00679363 0.00743311 0.0087513  ... 0.22267444 0.0078307  0.00767539]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.16 [%]
Global accuracy score (test) = 39.13 [%]
Global F1 score (train) = 33.36 [%]
Global F1 score (test) = 31.53 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.83      0.32       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.06      0.01      0.01       161
          DE PIE BARRIENDO       0.57      0.60      0.58       161
   DE PIE DOBLANDO TOALLAS       0.00      0.00      0.00       161
    DE PIE MOVIENDO LIBROS       0.26      0.47      0.34       161
          DE PIE USANDO PC       0.71      0.86      0.78       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       0.99      0.89      0.94       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.24      0.95      0.39       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.55      0.57      0.56       161
                    TROTAR       0.89      0.73      0.80       138

                  accuracy                           0.39      2392
                 macro avg       0.30      0.39      0.32      2392
              weighted avg       0.29      0.39      0.31      2392

2025-11-05 16:33:01.516723: 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 16:33:01.528039: 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 00:00:1762356781.541483 3710653 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356781.545516 3710653 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356781.555888 3710653 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356781.555909 3710653 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356781.555911 3710653 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356781.555912 3710653 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:33:01.559150: 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 00:00:1762356783.837639 3710653 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356785.262032 3710792 service.cc:152] XLA service 0x7e8da40090e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356785.262091 3710792 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:33:05.305470: 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 00:00:1762356785.434162 3710792 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356787.675167 3710792 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3137 - loss: 1.8309
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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.7596
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Epoch 16/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.7049 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7123
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Epoch 17/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.7599 
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[1m206/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7271
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.7258
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3418 - loss: 1.7249 - val_accuracy: 0.3773 - val_loss: 1.4861
Epoch 18/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.2500 - loss: 1.7331
[1m 45/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3285 - loss: 1.7174 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3378 - loss: 1.7082
[1m127/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.7045
[1m168/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7069
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7088
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7100
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Epoch 19/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3750 - loss: 1.5879
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[1m169/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.6957
[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.6961
[1m255/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.6967
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3505 - loss: 1.6970 - val_accuracy: 0.3881 - val_loss: 1.4812

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 715ms/step
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 744us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 14: 39.13 [%]
F1-score capturado en la ejecución 14: 31.53 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 64/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 800us/step  
[1m136/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 746us/step
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[1m491/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 719us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 715us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 741us/step
[1m138/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 735us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 38.81 [%]
Global F1 score (validation) = 32.97 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.00572075 0.00902629 0.00468081 ... 0.00291963 0.00720383 0.00027291]
 [0.00423478 0.00667865 0.00352672 ... 0.0034946  0.00408733 0.00032188]
 [0.0039464  0.00622832 0.00329344 ... 0.00355589 0.00399442 0.00031313]
 ...
 [0.00065362 0.00069994 0.00090078 ... 0.01086699 0.00910897 0.01443339]
 [0.00099229 0.00107432 0.00143922 ... 0.02341678 0.01048858 0.01492867]
 [0.00629241 0.00678327 0.009239   ... 0.19627748 0.00727389 0.00888383]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.33 [%]
Global accuracy score (test) = 39.26 [%]
Global F1 score (train) = 36.19 [%]
Global F1 score (test) = 33.99 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.86      0.33       161
 CAMINAR CON MÓVIL O LIBRO       0.12      0.01      0.02       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.47      0.55      0.50       161
   DE PIE DOBLANDO TOALLAS       0.26      0.24      0.25       161
    DE PIE MOVIENDO LIBROS       0.33      0.36      0.34       161
          DE PIE USANDO PC       0.73      0.66      0.69       161
        FASE REPOSO CON K5       0.28      0.87      0.42       161
INCREMENTAL CICLOERGOMETRO       0.97      0.84      0.90       161
           SENTADO LEYENDO       0.34      0.27      0.30       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.48      0.51       161
                    TROTAR       0.83      0.80      0.82       138

                  accuracy                           0.39      2392
                 macro avg       0.34      0.40      0.34      2392
              weighted avg       0.33      0.39      0.34      2392

2025-11-05 16:33:32.306806: 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 16:33:32.317994: 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 00:00:1762356812.331097 3713444 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356812.335211 3713444 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356812.345120 3713444 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356812.345140 3713444 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356812.345142 3713444 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356812.345144 3713444 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:33:32.348440: 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 00:00:1762356814.622164 3713444 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356816.036531 3713556 service.cc:152] XLA service 0x791bc801bfa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356816.036568 3713556 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:33:36.073790: 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 00:00:1762356816.192793 3713556 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356818.418015 3713556 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3142 - loss: 1.8112
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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3373 - loss: 1.7280
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[1m235/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.7300
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Epoch 15/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 1.6134
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3244 - loss: 1.7642 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3310 - loss: 1.7541
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[1m170/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.7440
[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3358 - loss: 1.7431
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3363 - loss: 1.7421
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3364 - loss: 1.7412 - val_accuracy: 0.3791 - val_loss: 1.4738
Epoch 16/98

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[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3313 - loss: 1.7582 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.7450
[1m127/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3317 - loss: 1.7361
[1m170/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.7303
[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3359 - loss: 1.7270
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.7254
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3372 - loss: 1.7251 - val_accuracy: 0.3919 - val_loss: 1.4684
Epoch 17/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.7169 
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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.7087
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[1m254/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.6966
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3425 - loss: 1.6963 - val_accuracy: 0.4018 - val_loss: 1.4537
Epoch 21/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3538 - loss: 1.6626 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.6748
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[1m162/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.6844
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.6865
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.6872
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3508 - loss: 1.6873 - val_accuracy: 0.4095 - val_loss: 1.4406
Epoch 22/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.4375 - loss: 1.6798
[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3571 - loss: 1.6833 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.6822
[1m123/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.6815
[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.6818
[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.6812
[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3525 - loss: 1.6813
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3525 - loss: 1.6814 - val_accuracy: 0.4168 - val_loss: 1.4479
Epoch 23/98

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[1m 46/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3642 - loss: 1.6707 
[1m 91/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3599 - loss: 1.6758
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Epoch 24/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3281 - loss: 1.6368
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.6809 
[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3403 - loss: 1.6798
[1m127/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.6787
[1m171/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.6771
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.6756
[1m256/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.6749
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3477 - loss: 1.6745 - val_accuracy: 0.3955 - val_loss: 1.4553
Epoch 25/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3594 - loss: 1.7223
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3543 - loss: 1.6437 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.6440
[1m129/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.6453
[1m172/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3583 - loss: 1.6482
[1m215/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.6511
[1m259/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.6539
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3572 - loss: 1.6549 - val_accuracy: 0.3966 - val_loss: 1.4504
Epoch 26/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3281 - loss: 1.7928
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3589 - loss: 1.6337 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3611 - loss: 1.6382
[1m125/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3593 - loss: 1.6451
[1m169/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.6494
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.6515
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3571 - loss: 1.6523
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3569 - loss: 1.6532 - val_accuracy: 0.4213 - val_loss: 1.4425

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m55s[0m 746ms/step
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 761us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 15: 39.26 [%]
F1-score capturado en la ejecución 15: 33.99 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:01[0m 881ms/step
[1m 68/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 754us/step  
[1m140/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 727us/step
[1m217/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 699us/step
[1m289/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 701us/step
[1m356/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 711us/step
[1m428/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 711us/step
[1m502/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 707us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 788us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 743us/step
[1m134/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 758us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 42.13 [%]
Global F1 score (validation) = 36.57 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.1499552e-03 4.9440362e-03 3.5362998e-03 ... 2.4744156e-03
  7.2893598e-03 2.0995285e-04]
 [2.9688517e-03 3.2912956e-03 2.6472495e-03 ... 5.1257377e-03
  3.0340245e-03 1.7014636e-04]
 [3.1276753e-03 3.4778502e-03 2.8325715e-03 ... 5.0840289e-03
  2.9932277e-03 1.6855872e-04]
 ...
 [3.0215512e-04 2.8976754e-04 2.8951094e-04 ... 5.9967753e-03
  3.1599368e-03 1.0940351e-02]
 [2.2492580e-04 2.1309748e-04 2.1533262e-04 ... 4.7052000e-03
  2.6031183e-03 1.0942181e-02]
 [4.3855458e-03 4.2218179e-03 4.7318884e-03 ... 2.5342992e-01
  6.3741077e-03 9.4216513e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 45.54 [%]
Global accuracy score (test) = 45.03 [%]
Global F1 score (train) = 39.08 [%]
Global F1 score (test) = 38.41 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.29      0.06      0.10       161
       CAMINAR USUAL SPEED       0.30      0.67      0.41       161
            CAMINAR ZIGZAG       0.19      0.26      0.22       161
          DE PIE BARRIENDO       0.47      0.79      0.59       161
   DE PIE DOBLANDO TOALLAS       0.21      0.02      0.04       161
    DE PIE MOVIENDO LIBROS       0.26      0.25      0.26       161
          DE PIE USANDO PC       0.62      0.86      0.72       161
        FASE REPOSO CON K5       0.60      0.86      0.71       161
INCREMENTAL CICLOERGOMETRO       0.98      0.89      0.93       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.29      0.71      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.50      0.68      0.58       161
                    TROTAR       0.84      0.74      0.78       138

                  accuracy                           0.45      2392
                 macro avg       0.37      0.45      0.38      2392
              weighted avg       0.37      0.45      0.38      2392

2025-11-05 16:34:06.410839: 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 16:34:06.422222: 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 00:00:1762356846.435527 3716850 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356846.439825 3716850 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356846.449769 3716850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356846.449788 3716850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356846.449790 3716850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356846.449792 3716850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:34:06.452987: 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 00:00:1762356848.702576 3716850 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356850.099500 3716979 service.cc:152] XLA service 0x716b0400a440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356850.099535 3716979 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:34:10.134800: 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 00:00:1762356850.262900 3716979 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356852.567380 3716979 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3076 - loss: 1.8376
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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3343 - loss: 1.7030
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Epoch 16/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7216 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7104
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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3486 - loss: 1.6917 - val_accuracy: 0.3791 - val_loss: 1.4766
Epoch 21/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.6830 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.6906
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[1m172/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.6949
[1m215/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.6947
[1m256/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.6938
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3449 - loss: 1.6935 - val_accuracy: 0.3791 - val_loss: 1.4658
Epoch 22/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.6618 
[1m 76/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.6611
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[1m198/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.6659
[1m238/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.6679
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Epoch 23/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.6568 
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Epoch 24/98

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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3546 - loss: 1.6684
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.6691
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3549 - loss: 1.6695 - val_accuracy: 0.4022 - val_loss: 1.4641

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 709ms/step
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 751us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 16: 45.03 [%]
F1-score capturado en la ejecución 16: 38.41 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:04[0m 887ms/step
[1m 66/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 771us/step  
[1m139/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 729us/step
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[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 715us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 66/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 769us/step
[1m138/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 731us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 40.22 [%]
Global F1 score (validation) = 34.88 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.4522877e-03 5.6235804e-03 3.3024475e-03 ... 2.4700658e-03
  5.9498698e-03 4.6849679e-04]
 [3.3737384e-03 4.1332794e-03 2.5668959e-03 ... 3.7287241e-03
  3.4595113e-03 4.7973453e-04]
 [2.9802329e-03 3.6095090e-03 2.2651548e-03 ... 4.1437387e-03
  3.0684774e-03 5.4953992e-04]
 ...
 [3.7134631e-04 3.6672247e-04 3.2920035e-04 ... 4.8381709e-03
  4.7442480e-03 8.8538295e-03]
 [1.8163878e-04 1.7360102e-04 1.5327644e-04 ... 2.4905454e-03
  3.2090733e-03 5.2290023e-03]
 [7.2704395e-03 7.5813136e-03 8.5125100e-03 ... 2.2235900e-01
  8.2755266e-03 7.2080651e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 44.3 [%]
Global accuracy score (test) = 43.9 [%]
Global F1 score (train) = 38.65 [%]
Global F1 score (test) = 37.64 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.23      0.22      0.22       161
       CAMINAR USUAL SPEED       0.27      0.84      0.41       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.54      0.58      0.56       161
   DE PIE DOBLANDO TOALLAS       0.37      0.50      0.42       161
    DE PIE MOVIENDO LIBROS       0.27      0.08      0.12       161
          DE PIE USANDO PC       0.63      0.88      0.73       161
        FASE REPOSO CON K5       0.35      0.86      0.50       161
INCREMENTAL CICLOERGOMETRO       0.99      0.89      0.93       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.26      0.37      0.30       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.55      0.63      0.59       161
                    TROTAR       0.92      0.78      0.84       138

                  accuracy                           0.44      2392
                 macro avg       0.36      0.44      0.38      2392
              weighted avg       0.35      0.44      0.37      2392

2025-11-05 16:34:39.686672: 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 16:34:39.698034: 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 00:00:1762356879.711138 3720105 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356879.715253 3720105 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356879.724951 3720105 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356879.724969 3720105 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356879.724971 3720105 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356879.724972 3720105 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:34:39.728094: 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 00:00:1762356881.981094 3720105 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356883.430615 3720218 service.cc:152] XLA service 0x7d9514004bd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356883.430647 3720218 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:34:43.466379: 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 00:00:1762356883.589304 3720218 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356885.803550 3720218 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 2/98

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Epoch 3/98

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Epoch 4/98

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[1m199/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2504 - loss: 2.0034
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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3134 - loss: 1.8030
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[1m159/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.7956
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[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.7923
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3201 - loss: 1.7918 - val_accuracy: 0.3634 - val_loss: 1.4834
Epoch 11/98

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[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.7621
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[1m199/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.7595
[1m241/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3312 - loss: 1.7611
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Epoch 12/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3246 - loss: 1.7709
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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7390 
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Epoch 18/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.6994 
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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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[1m247/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.6827
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Epoch 22/98

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[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3596 - loss: 1.6756
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[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.6837
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3498 - loss: 1.6840 - val_accuracy: 0.3696 - val_loss: 1.4594
Epoch 23/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.6822 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3450 - loss: 1.6789
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[1m160/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.6830
[1m196/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.6835
[1m238/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.6838
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3457 - loss: 1.6839 - val_accuracy: 0.3806 - val_loss: 1.4542
Epoch 24/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.6349 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.6541
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Epoch 25/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3634 - loss: 1.6722
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[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3639 - loss: 1.6637
[1m206/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3627 - loss: 1.6652
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.6653
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3618 - loss: 1.6655 - val_accuracy: 0.3943 - val_loss: 1.4453
Epoch 26/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.2656 - loss: 1.6076
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3278 - loss: 1.6715 
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[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3412 - loss: 1.6721
[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.6726
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.6723
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3440 - loss: 1.6720 - val_accuracy: 0.3711 - val_loss: 1.4599
Epoch 27/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2969 - loss: 1.7929
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[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3609 - loss: 1.6682
[1m118/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.6646
[1m156/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3631 - loss: 1.6610
[1m196/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.6591
[1m239/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.6588
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3612 - loss: 1.6595 - val_accuracy: 0.4107 - val_loss: 1.4452
Epoch 28/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.4375 - loss: 1.5092
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3731 - loss: 1.6293 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3720 - loss: 1.6252
[1m115/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3695 - loss: 1.6297
[1m157/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3661 - loss: 1.6385
[1m196/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3637 - loss: 1.6447
[1m239/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.6482
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3619 - loss: 1.6498 - val_accuracy: 0.3453 - val_loss: 1.5204
Epoch 29/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 22ms/step - accuracy: 0.3594 - loss: 1.6663
[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.6457 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.6544
[1m116/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.6561
[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.6564
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.6566
[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3598 - loss: 1.6568
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3594 - loss: 1.6571 - val_accuracy: 0.3949 - val_loss: 1.4572

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 698ms/step
[1m49/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 17: 43.9 [%]
F1-score capturado en la ejecución 17: 37.64 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 726us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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[1m 72/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 713us/step
[1m139/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 733us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 39.49 [%]
Global F1 score (validation) = 32.71 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.00566734 0.006157   0.0061016  ... 0.00217067 0.00771239 0.00023721]
 [0.00321471 0.00336224 0.00334527 ... 0.00307108 0.0030145  0.00023523]
 [0.00352304 0.00373278 0.00366838 ... 0.00289712 0.00336827 0.00023265]
 ...
 [0.00044387 0.00048548 0.00040904 ... 0.00878553 0.00810063 0.00896795]
 [0.00026535 0.00029447 0.00025053 ... 0.00506493 0.00599789 0.00826552]
 [0.00748718 0.00748316 0.006917   ... 0.21373805 0.00800891 0.00474661]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 42.96 [%]
Global accuracy score (test) = 43.52 [%]
Global F1 score (train) = 36.1 [%]
Global F1 score (test) = 36.12 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.95      0.36       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.47      0.84      0.60       161
   DE PIE DOBLANDO TOALLAS       0.37      0.09      0.14       161
    DE PIE MOVIENDO LIBROS       0.31      0.23      0.26       161
          DE PIE USANDO PC       0.67      0.87      0.75       161
        FASE REPOSO CON K5       0.31      0.87      0.45       161
INCREMENTAL CICLOERGOMETRO       0.99      0.88      0.93       161
           SENTADO LEYENDO       0.54      0.56      0.55       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.61      0.54      0.57       161
                    TROTAR       0.86      0.75      0.80       138

                  accuracy                           0.44      2392
                 macro avg       0.36      0.44      0.36      2392
              weighted avg       0.35      0.44      0.36      2392

2025-11-05 16:35:15.567845: 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 16:35:15.579426: 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 00:00:1762356915.593137 3723814 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356915.597570 3723814 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356915.607880 3723814 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356915.607902 3723814 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356915.607904 3723814 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356915.607912 3723814 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:35:15.611188: 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 00:00:1762356917.877493 3723814 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356919.311677 3723945 service.cc:152] XLA service 0x7cb6c4009e30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356919.311730 3723945 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:35:19.359329: 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 00:00:1762356919.478899 3723945 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356921.750008 3723945 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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[1m 75/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0958 - loss: 3.9037
[1m118/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0982 - loss: 3.7725
[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1001 - loss: 3.6661
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1024 - loss: 3.5776
[1m241/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1045 - loss: 3.5076
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.1065 - loss: 3.4530
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.1066 - loss: 3.4515 - val_accuracy: 0.2820 - val_loss: 2.0779
Epoch 2/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1608 - loss: 2.4660 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1617 - loss: 2.4352
[1m120/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1635 - loss: 2.4185
[1m159/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1657 - loss: 2.4057
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1681 - loss: 2.3906
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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2890 - loss: 1.8836
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Epoch 8/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3070 - loss: 1.8710
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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3252 - loss: 1.7665
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.7604 - val_accuracy: 0.3642 - val_loss: 1.4973
Epoch 14/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7734 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3377 - loss: 1.7678
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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.6864 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.6907
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Epoch 20/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.7121 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7057
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[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.6976
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Epoch 21/98

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Epoch 22/98

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Epoch 23/98

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Epoch 24/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.6930
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3443 - loss: 1.6843 - val_accuracy: 0.3785 - val_loss: 1.4443
Epoch 25/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.6863 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.6914
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[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.6889
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3502 - loss: 1.6878
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3506 - loss: 1.6873 - val_accuracy: 0.4125 - val_loss: 1.4648
Epoch 26/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.6916 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.6844
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[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.6802
[1m252/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.6786
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Epoch 27/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3546 - loss: 1.6807 - val_accuracy: 0.3832 - val_loss: 1.4751
Epoch 28/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 22ms/step - accuracy: 0.3750 - loss: 1.7142
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[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3563 - loss: 1.6574
[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3568 - loss: 1.6565
[1m205/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.6567
[1m244/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.6573
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Epoch 29/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.5954
[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3635 - loss: 1.6332 
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[1m206/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3642 - loss: 1.6428
[1m247/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3638 - loss: 1.6453
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3637 - loss: 1.6465 - val_accuracy: 0.4034 - val_loss: 1.4839

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 700ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 720us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 43.52 [%]
F1-score capturado en la ejecución 18: 36.12 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:00[0m 880ms/step
[1m 67/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 769us/step  
[1m142/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 717us/step
[1m218/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 697us/step
[1m293/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 690us/step
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[1m437/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 694us/step
[1m510/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 694us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 801us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 70/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 729us/step
[1m142/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 715us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 40.34 [%]
Global F1 score (validation) = 33.57 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.00893855 0.00731501 0.00617963 ... 0.00383931 0.01824845 0.00111478]
 [0.00408503 0.00350773 0.00307431 ... 0.00449728 0.00434951 0.00078625]
 [0.00373785 0.00324751 0.00284734 ... 0.00469597 0.00385452 0.00074355]
 ...
 [0.00171986 0.00176378 0.00191969 ... 0.06676148 0.012665   0.00400409]
 [0.00041859 0.00039683 0.00042127 ... 0.00886354 0.00563601 0.00109098]
 [0.00489569 0.00600119 0.00596357 ... 0.22021411 0.0065474  0.00402294]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 42.45 [%]
Global accuracy score (test) = 41.93 [%]
Global F1 score (train) = 34.88 [%]
Global F1 score (test) = 33.99 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.89      0.35       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.49      0.83      0.61       161
   DE PIE DOBLANDO TOALLAS       0.34      0.20      0.25       161
    DE PIE MOVIENDO LIBROS       0.43      0.04      0.07       161
          DE PIE USANDO PC       0.55      0.91      0.69       161
        FASE REPOSO CON K5       0.98      0.29      0.44       161
INCREMENTAL CICLOERGOMETRO       0.97      0.84      0.90       161
           SENTADO LEYENDO       0.27      1.00      0.43       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.49      0.60      0.54       161
                    TROTAR       0.89      0.75      0.82       138

                  accuracy                           0.42      2392
                 macro avg       0.38      0.42      0.34      2392
              weighted avg       0.37      0.42      0.34      2392

2025-11-05 16:35:51.228430: 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 16:35:51.239797: 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 00:00:1762356951.253351 3727510 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356951.257608 3727510 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356951.267430 3727510 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356951.267452 3727510 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356951.267454 3727510 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356951.267456 3727510 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:35:51.270660: 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 00:00:1762356953.541284 3727510 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356954.959672 3727641 service.cc:152] XLA service 0x7825380094f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356954.959744 3727641 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:35:55.006004: 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 00:00:1762356955.132829 3727641 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356957.462425 3727641 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3006 - loss: 1.8056
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[1m215/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3052 - loss: 1.8058
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Epoch 10/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3190 - loss: 1.7956 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3211 - loss: 1.7905
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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3291 - loss: 1.7347
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Epoch 17/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3435 - loss: 1.7030 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7091
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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.6917
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[1m198/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.6894
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3492 - loss: 1.6887 - val_accuracy: 0.3650 - val_loss: 1.4768
Epoch 22/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.6813 
[1m 89/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.6710
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[1m175/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.6721
[1m215/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3572 - loss: 1.6728
[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3570 - loss: 1.6732
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3567 - loss: 1.6737 - val_accuracy: 0.3840 - val_loss: 1.4642
Epoch 23/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3682 - loss: 1.6435 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3607 - loss: 1.6568
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Epoch 24/98

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[1m 45/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.6580 
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[1m217/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3494 - loss: 1.6719
[1m258/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.6739
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3498 - loss: 1.6744 - val_accuracy: 0.3820 - val_loss: 1.4479
Epoch 25/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3581 - loss: 1.6600
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[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3548 - loss: 1.6702
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.6720
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3547 - loss: 1.6735 - val_accuracy: 0.4229 - val_loss: 1.4632
Epoch 26/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.6351
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Epoch 27/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.4688 - loss: 1.6097
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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3752 - loss: 1.6234
[1m129/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3706 - loss: 1.6334
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[1m216/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3675 - loss: 1.6402
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3663 - loss: 1.6422 - val_accuracy: 0.3783 - val_loss: 1.4667
Epoch 28/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.6699 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.6573
[1m117/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3525 - loss: 1.6562
[1m156/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3535 - loss: 1.6580
[1m200/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3538 - loss: 1.6590
[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3543 - loss: 1.6593
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3547 - loss: 1.6588 - val_accuracy: 0.4034 - val_loss: 1.4578
Epoch 29/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3125 - loss: 1.9245
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.7062 
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[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.6687
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 712ms/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 817us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 19: 41.93 [%]
F1-score capturado en la ejecución 19: 33.99 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:59[0m 879ms/step
[1m 67/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 760us/step  
[1m134/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 754us/step
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 843us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 808us/step
[1m134/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 755us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 36.96 [%]
Global F1 score (validation) = 29.81 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.90344781e-03 5.56146912e-03 3.37168272e-03 ... 3.44570843e-03
  7.09080696e-03 2.76682113e-04]
 [3.25069716e-03 3.86027643e-03 2.35493854e-03 ... 3.66096501e-03
  2.46707071e-03 2.50288111e-04]
 [3.28821572e-03 3.91041115e-03 2.37289397e-03 ... 3.76004959e-03
  2.64555425e-03 2.58731656e-04]
 ...
 [7.95742162e-05 1.19439224e-04 9.56597869e-05 ... 2.33178679e-03
  2.03797943e-03 9.31699388e-03]
 [5.58746746e-04 8.58440122e-04 6.68829307e-04 ... 1.40713360e-02
  6.48755301e-03 8.91467091e-03]
 [6.89391838e-03 9.83317755e-03 9.60336998e-03 ... 2.41792142e-01
  6.74884720e-03 8.71910434e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.98 [%]
Global accuracy score (test) = 39.67 [%]
Global F1 score (train) = 34.2 [%]
Global F1 score (test) = 31.43 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.83      0.33       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.25      0.03      0.06       161
          DE PIE BARRIENDO       0.46      0.85      0.60       161
   DE PIE DOBLANDO TOALLAS       0.27      0.07      0.12       161
    DE PIE MOVIENDO LIBROS       0.30      0.14      0.19       161
          DE PIE USANDO PC       0.56      0.86      0.68       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       0.99      0.84      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.25      1.00      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.59      0.61      0.60       161
                    TROTAR       0.91      0.76      0.83       138

                  accuracy                           0.40      2392
                 macro avg       0.32      0.40      0.31      2392
              weighted avg       0.31      0.40      0.31      2392

2025-11-05 16:36:26.930709: 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 16:36:26.942249: 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 00:00:1762356986.956243 3731205 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762356986.960502 3731205 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762356986.970979 3731205 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356986.971001 3731205 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356986.971003 3731205 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762356986.971005 3731205 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:36:26.974345: 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 00:00:1762356989.262632 3731205 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762356990.682081 3731345 service.cc:152] XLA service 0x74ffb801aa10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762356990.682110 3731345 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:36:30.715612: 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 00:00:1762356990.839439 3731345 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762356993.118805 3731345 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:30[0m 3s/step - accuracy: 0.0156 - loss: 4.5661
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0860 - loss: 3.9580  
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0928 - loss: 3.8135
[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0988 - loss: 3.6918
[1m169/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1026 - loss: 3.5937
[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1060 - loss: 3.5093
[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1090 - loss: 3.4394
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.1104 - loss: 3.4069
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.1104 - loss: 3.4054 - val_accuracy: 0.2609 - val_loss: 2.0822
Epoch 2/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1703 - loss: 2.4249 
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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.8951
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Epoch 7/98

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[1m 75/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2994 - loss: 1.8461
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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7470
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[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3327 - loss: 1.7553
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Epoch 13/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3264 - loss: 1.7522 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3315 - loss: 1.7515
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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.7170 
[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3450 - loss: 1.7199
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[1m192/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7261
[1m238/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7246
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3419 - loss: 1.7236 - val_accuracy: 0.4190 - val_loss: 1.4456
Epoch 19/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7092 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.7031
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7006
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[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7018
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Epoch 20/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3576 - loss: 1.6598 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.6728
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Epoch 21/98

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Epoch 22/98

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Epoch 23/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3578 - loss: 1.6665 - val_accuracy: 0.4038 - val_loss: 1.4434
Epoch 24/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.4062 - loss: 1.9177
[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3785 - loss: 1.6736 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3714 - loss: 1.6641
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[1m162/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3672 - loss: 1.6590
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3663 - loss: 1.6586
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3657 - loss: 1.6593
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3654 - loss: 1.6599 - val_accuracy: 0.3994 - val_loss: 1.4449
Epoch 25/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.5449
[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3650 - loss: 1.6372 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.6475
[1m127/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3623 - loss: 1.6517
[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.6541
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3607 - loss: 1.6558
[1m252/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3601 - loss: 1.6572
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3598 - loss: 1.6579 - val_accuracy: 0.3964 - val_loss: 1.4592
Epoch 26/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.6842 
[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.6732
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[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.6680
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3592 - loss: 1.6675
[1m240/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3598 - loss: 1.6670
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Epoch 27/98

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Epoch 28/98

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Epoch 29/98

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Epoch 30/98

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[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3633 - loss: 1.6463
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Epoch 31/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7434 
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Epoch 32/98

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 701ms/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 815us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 39.67 [%]
F1-score capturado en la ejecución 20: 31.43 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:23[0m 921ms/step
[1m 66/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 771us/step  
[1m146/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 695us/step
[1m223/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 680us/step
[1m300/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 673us/step
[1m373/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 677us/step
[1m442/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 685us/step
[1m512/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 689us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 728us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 70/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 732us/step
[1m149/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 683us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 40.69 [%]
Global F1 score (validation) = 33.56 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.1514353e-03 7.7995458e-03 4.3109679e-03 ... 2.6493354e-03
  6.0417261e-03 3.9446773e-04]
 [2.7153178e-03 4.1570100e-03 2.6268789e-03 ... 4.3662670e-03
  2.5257713e-03 4.8277582e-04]
 [3.2718114e-03 4.9839052e-03 3.1784542e-03 ... 4.6887482e-03
  2.5304742e-03 5.3655857e-04]
 ...
 [6.1301718e-05 6.2085164e-05 7.3923824e-05 ... 1.9792675e-03
  3.0896796e-03 2.9766737e-03]
 [9.7230681e-05 1.0455632e-04 1.1420042e-04 ... 3.3255445e-03
  4.4977688e-03 2.3950154e-03]
 [5.2069309e-03 6.3671381e-03 7.2792345e-03 ... 2.7158982e-01
  6.1680512e-03 5.8883019e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 45.52 [%]
Global accuracy score (test) = 43.73 [%]
Global F1 score (train) = 38.19 [%]
Global F1 score (test) = 36.09 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.86      0.32       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.51      0.55      0.53       161
   DE PIE DOBLANDO TOALLAS       0.36      0.46      0.41       161
    DE PIE MOVIENDO LIBROS       0.00      0.00      0.00       161
          DE PIE USANDO PC       0.53      0.91      0.67       161
        FASE REPOSO CON K5       0.66      0.71      0.68       161
INCREMENTAL CICLOERGOMETRO       0.99      0.89      0.94       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.34      0.95      0.50       161
   SUBIR Y BAJAR ESCALERAS       0.57      0.52      0.54       161
                    TROTAR       0.91      0.76      0.83       138

                  accuracy                           0.44      2392
                 macro avg       0.34      0.44      0.36      2392
              weighted avg       0.33      0.44      0.36      2392

2025-11-05 16:37:04.348790: 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 16:37:04.360588: 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 00:00:1762357024.373747 3735185 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357024.377701 3735185 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357024.387633 3735185 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357024.387652 3735185 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357024.387654 3735185 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357024.387656 3735185 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:37:04.390641: 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 00:00:1762357026.642201 3735185 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357028.044361 3735318 service.cc:152] XLA service 0x7ea3c000a600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357028.044397 3735318 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:37:08.077513: 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 00:00:1762357028.196684 3735318 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357030.457805 3735318 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:20[0m 3s/step - accuracy: 0.0625 - loss: 4.4526
[1m 34/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0735 - loss: 4.0074  
[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0837 - loss: 3.8432
[1m120/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0899 - loss: 3.7162
[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0945 - loss: 3.6162
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0990 - loss: 3.5269
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1033 - loss: 3.4509
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.1059 - loss: 3.4038 - val_accuracy: 0.2168 - val_loss: 2.0441
Epoch 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2727 - loss: 1.8879
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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3231 - loss: 1.7836 - val_accuracy: 0.3986 - val_loss: 1.4894
Epoch 12/98

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[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3119 - loss: 1.7838 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3105 - loss: 1.7846
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3173 - loss: 1.7843 - val_accuracy: 0.3708 - val_loss: 1.5029
Epoch 13/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3365 - loss: 1.7462 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3309 - loss: 1.7608
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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3251 - loss: 1.7167 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3319 - loss: 1.7146
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Epoch 19/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.6511 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3375 - loss: 1.6645
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Epoch 20/98

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Epoch 21/98

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Epoch 22/98

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Epoch 23/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.6515 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.6596
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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.6738
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.6757
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3477 - loss: 1.6765 - val_accuracy: 0.3844 - val_loss: 1.4730
Epoch 24/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7088 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7031
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[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.6978
[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.6962
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.6939
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3435 - loss: 1.6927 - val_accuracy: 0.4095 - val_loss: 1.4490
Epoch 25/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.7198 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3570 - loss: 1.7080
[1m123/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.7030
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[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.7008
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.6989
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Epoch 26/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3920 - loss: 1.6080 
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Epoch 27/98

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Epoch 28/98

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Epoch 29/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3700 - loss: 1.6378 
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 722ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 783us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 21: 43.73 [%]
F1-score capturado en la ejecución 21: 36.09 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m143/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 716us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 38.32 [%]
Global F1 score (validation) = 34.38 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[3.73376929e-03 6.22341037e-03 3.71874450e-03 ... 2.75782426e-03
  6.11427007e-03 1.90345134e-04]
 [3.51028075e-03 5.81829669e-03 3.87739972e-03 ... 4.26957989e-03
  2.78143934e-03 2.42477923e-04]
 [3.23208654e-03 5.38941612e-03 3.59424995e-03 ... 4.32213582e-03
  2.38510570e-03 2.24613177e-04]
 ...
 [1.37955692e-04 9.45965294e-05 1.24967613e-04 ... 2.29742564e-03
  3.17457574e-03 6.36657467e-03]
 [4.25040518e-04 3.05577851e-04 4.11334884e-04 ... 1.05276825e-02
  5.24933822e-03 1.04556866e-02]
 [6.54863659e-03 6.49382919e-03 8.42391606e-03 ... 2.26831317e-01
  6.23938441e-03 6.88258884e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 44.27 [%]
Global accuracy score (test) = 40.26 [%]
Global F1 score (train) = 40.58 [%]
Global F1 score (test) = 36.01 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.37      0.26       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.24      0.25       161
       CAMINAR USUAL SPEED       0.29      0.02      0.05       161
            CAMINAR ZIGZAG       0.24      0.29      0.26       161
          DE PIE BARRIENDO       0.53      0.61      0.57       161
   DE PIE DOBLANDO TOALLAS       0.31      0.27      0.29       161
    DE PIE MOVIENDO LIBROS       0.30      0.09      0.14       161
          DE PIE USANDO PC       0.54      0.94      0.68       161
        FASE REPOSO CON K5       0.26      0.87      0.40       161
INCREMENTAL CICLOERGOMETRO       0.99      0.83      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.22      0.14      0.17       161
   SUBIR Y BAJAR ESCALERAS       0.57      0.65      0.60       161
                    TROTAR       0.89      0.77      0.82       138

                  accuracy                           0.40      2392
                 macro avg       0.37      0.41      0.36      2392
              weighted avg       0.37      0.40      0.36      2392

2025-11-05 16:37:40.116112: 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 16:37:40.127566: 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 00:00:1762357060.141046 3738881 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357060.145349 3738881 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357060.155356 3738881 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357060.155375 3738881 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357060.155377 3738881 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357060.155379 3738881 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:37:40.158735: 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 00:00:1762357062.432925 3738881 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357063.857142 3739012 service.cc:152] XLA service 0x766bec009460 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357063.857172 3739012 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:37:43.890618: 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 00:00:1762357064.013676 3739012 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357066.243466 3739012 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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[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.0979 - loss: 3.4925
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.0994 - loss: 3.4545
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.0995 - loss: 3.4530 - val_accuracy: 0.2757 - val_loss: 2.1240
Epoch 2/98

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[1m216/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1677 - loss: 2.3958
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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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[1m 89/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3272 - loss: 1.7412
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Epoch 15/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3222 - loss: 1.7368 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3274 - loss: 1.7381
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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.6947
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[1m201/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3534 - loss: 1.6966
[1m244/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.6962
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3526 - loss: 1.6958 - val_accuracy: 0.3986 - val_loss: 1.4554
Epoch 21/98

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[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.6831 
[1m 88/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.6834
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[1m214/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.6919
[1m255/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.6918
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Epoch 22/98

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Epoch 23/98

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Epoch 24/98

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Epoch 25/98

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[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.6596
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[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3635 - loss: 1.6683
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.6699
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3612 - loss: 1.6706 - val_accuracy: 0.3800 - val_loss: 1.4553
Epoch 26/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3906 - loss: 1.6722
[1m 45/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3449 - loss: 1.7078 
[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.6892
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[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.6755
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.6725
[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.6709
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3527 - loss: 1.6706 - val_accuracy: 0.3881 - val_loss: 1.4652
Epoch 27/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8470
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3654 - loss: 1.6765 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3628 - loss: 1.6739
[1m123/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.6754
[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.6752
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.6744
[1m242/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.6727
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3604 - loss: 1.6716 - val_accuracy: 0.3964 - val_loss: 1.4598
Epoch 28/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3494 - loss: 1.6876 
[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.6770
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[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.6661
[1m254/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3557 - loss: 1.6652
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3559 - loss: 1.6646 - val_accuracy: 0.4200 - val_loss: 1.4679
Epoch 29/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2969 - loss: 1.7557
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[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3600 - loss: 1.6566
[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3591 - loss: 1.6577
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Epoch 30/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7090
[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.6078 
[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.6157
[1m118/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.6195
[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3623 - loss: 1.6239
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.6268
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.6302
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3615 - loss: 1.6318 - val_accuracy: 0.3587 - val_loss: 1.5022

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 696ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 782us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 22: 40.26 [%]
F1-score capturado en la ejecución 22: 36.01 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:59[0m 878ms/step
[1m 68/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 751us/step  
[1m140/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 722us/step
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[1m501/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 704us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 68/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 755us/step
[1m135/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 754us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 35.87 [%]
Global F1 score (validation) = 28.99 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.26024850e-03 5.48489951e-03 3.08121904e-03 ... 1.64179259e-03
  5.20362193e-03 1.58096809e-04]
 [3.15979915e-03 4.13905457e-03 2.39125453e-03 ... 2.55617592e-03
  3.65032977e-03 2.06632176e-04]
 [3.16491607e-03 4.15471243e-03 2.40071141e-03 ... 2.80473451e-03
  3.58748110e-03 2.32368577e-04]
 ...
 [1.18530916e-04 1.23224323e-04 1.68885454e-04 ... 4.17752005e-03
  3.51718883e-03 7.98256509e-03]
 [7.58050810e-05 7.73553693e-05 1.05440027e-04 ... 2.79331533e-03
  2.81027984e-03 7.71847460e-03]
 [6.27108244e-03 7.57612241e-03 8.00896529e-03 ... 2.22440407e-01
  6.47663884e-03 7.26351002e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 40.23 [%]
Global accuracy score (test) = 38.63 [%]
Global F1 score (train) = 33.87 [%]
Global F1 score (test) = 31.77 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.10      0.12       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.22      0.80      0.35       161
          DE PIE BARRIENDO       0.39      0.98      0.56       161
   DE PIE DOBLANDO TOALLAS       0.00      0.00      0.00       161
    DE PIE MOVIENDO LIBROS       0.25      0.31      0.28       161
          DE PIE USANDO PC       0.67      0.19      0.30       161
        FASE REPOSO CON K5       0.29      0.87      0.44       161
INCREMENTAL CICLOERGOMETRO       1.00      0.83      0.91       161
           SENTADO LEYENDO       0.44      0.42      0.43       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.56      0.57       161
                    TROTAR       0.85      0.80      0.82       138

                  accuracy                           0.39      2392
                 macro avg       0.32      0.39      0.32      2392
              weighted avg       0.32      0.39      0.31      2392

2025-11-05 16:38:16.359254: 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 16:38:16.370588: 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 00:00:1762357096.383944 3742694 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357096.388041 3742694 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357096.397804 3742694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357096.397823 3742694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357096.397825 3742694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357096.397827 3742694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:38:16.400933: 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 00:00:1762357098.641682 3742694 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357100.057148 3742804 service.cc:152] XLA service 0x7b09d8009d90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357100.057209 3742804 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:38:20.100449: 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 00:00:1762357100.219311 3742804 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357102.497613 3742804 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 2/98

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Epoch 3/98

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Epoch 4/98

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[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 1.9953
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Epoch 5/98

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[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 1.9155
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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3115 - loss: 1.7984
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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.6998
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[1m254/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7067
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Epoch 17/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.7295 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7271
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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.6866
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Epoch 21/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.6785
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[1m213/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.6900
[1m256/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.6903
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3495 - loss: 1.6904 - val_accuracy: 0.3941 - val_loss: 1.4521
Epoch 22/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.4375 - loss: 1.5821
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3822 - loss: 1.6658 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.6757
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3700 - loss: 1.6773
[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3670 - loss: 1.6776
[1m201/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3645 - loss: 1.6785
[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3627 - loss: 1.6792
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3617 - loss: 1.6794 - val_accuracy: 0.3848 - val_loss: 1.4707
Epoch 23/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.6944 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.6892
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[1m212/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3540 - loss: 1.6784
[1m252/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.6764
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Epoch 24/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.6844 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3576 - loss: 1.6845
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Epoch 25/98

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Epoch 26/98

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Epoch 27/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3658 - loss: 1.6506 - val_accuracy: 0.3913 - val_loss: 1.4470
Epoch 28/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.6722 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.6659
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[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.6560
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3589 - loss: 1.6551
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3589 - loss: 1.6551 - val_accuracy: 0.3939 - val_loss: 1.4570
Epoch 29/98

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[1m 36/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3645 - loss: 1.6574 
[1m 76/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3636 - loss: 1.6530
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[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.6513
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Epoch 30/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3635 - loss: 1.6524 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3630 - loss: 1.6454
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Epoch 31/98

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Epoch 32/98

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Epoch 33/98

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Epoch 34/98

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Epoch 35/98

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Epoch 36/98

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Epoch 37/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8608
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[1m200/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3752 - loss: 1.6248
[1m240/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3751 - loss: 1.6253
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3751 - loss: 1.6260 - val_accuracy: 0.4059 - val_loss: 1.4527

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 725ms/step
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 710us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 23: 38.63 [%]
F1-score capturado en la ejecución 23: 31.77 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:56[0m 873ms/step
[1m 68/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 751us/step  
[1m130/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 778us/step
[1m197/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 768us/step
[1m263/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 766us/step
[1m333/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 755us/step
[1m406/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 743us/step
[1m480/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 734us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 758us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 68/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 751us/step
[1m135/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 751us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 40.59 [%]
Global F1 score (validation) = 33.37 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[6.1575538e-03 6.2957746e-03 4.0379991e-03 ... 2.4288814e-03
  7.8477608e-03 1.8825901e-04]
 [3.5352330e-03 3.7798570e-03 2.7360402e-03 ... 3.7800826e-03
  2.1002721e-03 2.6367616e-04]
 [3.8013214e-03 4.0238998e-03 2.8741516e-03 ... 3.8738789e-03
  2.7741352e-03 2.9827395e-04]
 ...
 [1.8081878e-04 1.7552052e-04 1.6160242e-04 ... 3.5015915e-03
  3.6527636e-03 5.4320316e-03]
 [4.4818396e-05 4.3647073e-05 3.9411010e-05 ... 1.1074976e-03
  1.2250217e-03 3.5143376e-03]
 [9.2856167e-03 1.0598275e-02 1.2650331e-02 ... 2.5207216e-01
  1.0532383e-02 7.8519164e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 43.56 [%]
Global accuracy score (test) = 40.8 [%]
Global F1 score (train) = 36.19 [%]
Global F1 score (test) = 32.75 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.21      0.92      0.35       161
          DE PIE BARRIENDO       0.49      0.79      0.60       161
   DE PIE DOBLANDO TOALLAS       0.42      0.34      0.37       161
    DE PIE MOVIENDO LIBROS       0.33      0.02      0.04       161
          DE PIE USANDO PC       0.55      0.90      0.68       161
        FASE REPOSO CON K5       1.00      0.14      0.25       161
INCREMENTAL CICLOERGOMETRO       0.97      0.86      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.27      1.00      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.44      0.49       161
                    TROTAR       0.85      0.77      0.81       138

                  accuracy                           0.41      2392
                 macro avg       0.38      0.41      0.33      2392
              weighted avg       0.37      0.41      0.32      2392

2025-11-05 16:38:56.131940: 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 16:38:56.143182: 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 00:00:1762357136.156184 3747146 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357136.160366 3747146 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357136.170160 3747146 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357136.170180 3747146 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357136.170182 3747146 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357136.170184 3747146 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:38:56.173365: 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 00:00:1762357138.441278 3747146 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357139.860043 3747255 service.cc:152] XLA service 0x7ba07c009600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357139.860085 3747255 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:38:59.893685: 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 00:00:1762357140.016049 3747255 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357142.279828 3747255 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3200 - loss: 1.7903 - val_accuracy: 0.4148 - val_loss: 1.4898
Epoch 11/98

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[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3275 - loss: 1.7678 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3302 - loss: 1.7641
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[1m216/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3300 - loss: 1.7654
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3288 - loss: 1.7671 - val_accuracy: 0.4188 - val_loss: 1.4843
Epoch 12/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3297 - loss: 1.7098 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3274 - loss: 1.7343
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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3540 - loss: 1.7183
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Epoch 18/98

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[1m 45/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7187 
[1m 89/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7210
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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.6957
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Epoch 22/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3327 - loss: 1.6895 
[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.7025
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[1m242/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3403 - loss: 1.7090
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3411 - loss: 1.7077 - val_accuracy: 0.3753 - val_loss: 1.4692
Epoch 23/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.6872 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.6770
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[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.6746
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.6750
[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.6762
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3476 - loss: 1.6773 - val_accuracy: 0.3723 - val_loss: 1.4744
Epoch 24/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3595 - loss: 1.6872 
[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.6836
[1m120/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.6806
[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.6790
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.6788
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.6787
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Epoch 25/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.6828 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.6820
[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.6780
[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.6767
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.6758
[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.6756
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3476 - loss: 1.6755 - val_accuracy: 0.4150 - val_loss: 1.4646

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 706ms/step
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 729us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 24: 40.8 [%]
F1-score capturado en la ejecución 24: 32.75 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:58[0m 877ms/step
[1m 68/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 756us/step  
[1m136/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 747us/step
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[1m280/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 722us/step
[1m353/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 716us/step
[1m425/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 715us/step
[1m494/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 717us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 795us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 66/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 775us/step
[1m134/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 755us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 41.5 [%]
Global F1 score (validation) = 36.55 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.2832396e-03 4.8963223e-03 3.4345679e-03 ... 1.9305963e-03
  4.8854779e-03 1.7027601e-04]
 [3.2735458e-03 3.7982380e-03 2.7852284e-03 ... 3.3931560e-03
  2.2691553e-03 2.2659838e-04]
 [2.9900817e-03 3.5283992e-03 2.6223199e-03 ... 4.3023704e-03
  1.7877083e-03 2.5289302e-04]
 ...
 [8.3872187e-04 7.3849736e-04 7.4150192e-04 ... 1.9745922e-02
  7.8155454e-03 7.1558133e-03]
 [3.0674442e-04 2.5903713e-04 2.6440120e-04 ... 8.3599985e-03
  4.2171264e-03 5.6533925e-03]
 [6.0164412e-03 5.9787044e-03 6.2647853e-03 ... 2.3892038e-01
  8.7616527e-03 5.9067514e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 45.02 [%]
Global accuracy score (test) = 44.98 [%]
Global F1 score (train) = 40.68 [%]
Global F1 score (test) = 39.32 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       161
       CAMINAR USUAL SPEED       0.24      0.78      0.37       161
            CAMINAR ZIGZAG       0.19      0.12      0.15       161
          DE PIE BARRIENDO       0.45      0.55      0.49       161
   DE PIE DOBLANDO TOALLAS       0.24      0.14      0.17       161
    DE PIE MOVIENDO LIBROS       0.27      0.21      0.24       161
          DE PIE USANDO PC       0.56      0.86      0.68       161
        FASE REPOSO CON K5       0.67      0.82      0.74       161
INCREMENTAL CICLOERGOMETRO       0.99      0.84      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.36      0.84      0.50       161
      SENTADO VIENDO LA TV       0.41      0.14      0.21       161
   SUBIR Y BAJAR ESCALERAS       0.55      0.75      0.63       161
                    TROTAR       0.90      0.75      0.82       138

                  accuracy                           0.45      2392
                 macro avg       0.39      0.45      0.39      2392
              weighted avg       0.38      0.45      0.39      2392

2025-11-05 16:39:29.939863: 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 16:39:29.951484: 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 00:00:1762357169.964795 3750467 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357169.968965 3750467 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357169.979092 3750467 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357169.979114 3750467 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357169.979116 3750467 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357169.979117 3750467 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:39:29.982335: 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 00:00:1762357172.229762 3750467 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357173.652812 3750595 service.cc:152] XLA service 0x75b31001b5f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357173.652861 3750595 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:39:33.692738: 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 00:00:1762357173.810878 3750595 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357176.095528 3750595 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:32[0m 3s/step - accuracy: 0.0781 - loss: 4.4153
[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0833 - loss: 3.9486  
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0880 - loss: 3.7922
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Epoch 2/98

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Epoch 3/98

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Epoch 4/98

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[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 1.9979
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Epoch 5/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2880 - loss: 1.8914 
[1m 89/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2812 - loss: 1.9016
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[1m221/274[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2764 - loss: 1.9075
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Epoch 6/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2842 - loss: 1.8945 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2844 - loss: 1.8847
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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3311 - loss: 1.7573
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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7328
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3483 - loss: 1.7150 - val_accuracy: 0.4125 - val_loss: 1.4382
Epoch 18/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.6954 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.6985
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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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Epoch 22/98

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Saved model to disk.

Accuracy capturado en la ejecución 25: 44.98 [%]
F1-score capturado en la ejecución 25: 39.32 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m149/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 680us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 38.04 [%]
Global F1 score (validation) = 31.32 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.2831656e-03 8.3337100e-03 5.0522746e-03 ... 2.8048425e-03
  9.6919117e-03 2.7841379e-04]
 [3.9291722e-03 4.1341912e-03 2.9053660e-03 ... 4.5172209e-03
  4.6866382e-03 4.0065820e-04]
 [3.0788702e-03 3.1983403e-03 2.2595678e-03 ... 4.4536134e-03
  3.6677178e-03 3.0960527e-04]
 ...
 [2.3044832e-04 2.0339224e-04 2.1919746e-04 ... 2.6811815e-03
  4.9851164e-03 5.2187573e-03]
 [3.7530781e-04 3.4412139e-04 3.7081508e-04 ... 5.6736651e-03
  5.8898940e-03 4.5781257e-03]
 [5.5416655e-03 6.1904346e-03 6.8675927e-03 ... 2.4139813e-01
  6.0798908e-03 5.9301672e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.99 [%]
Global accuracy score (test) = 41.1 [%]
Global F1 score (train) = 35.09 [%]
Global F1 score (test) = 33.64 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.17      0.09      0.12       161
       CAMINAR USUAL SPEED       0.26      0.90      0.40       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.44      0.88      0.59       161
   DE PIE DOBLANDO TOALLAS       0.00      0.00      0.00       161
    DE PIE MOVIENDO LIBROS       0.28      0.25      0.26       161
          DE PIE USANDO PC       0.69      0.81      0.75       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       0.99      0.88      0.93       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.24      0.80      0.37       161
      SENTADO VIENDO LA TV       0.24      0.14      0.18       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.67      0.60       161
                    TROTAR       0.92      0.80      0.85       138

                  accuracy                           0.41      2392
                 macro avg       0.32      0.41      0.34      2392
              weighted avg       0.31      0.41      0.33      2392

2025-11-05 16:40:02.255170: 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 16:40:02.266245: 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 00:00:1762357202.279456 3753527 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357202.283575 3753527 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357202.293654 3753527 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357202.293673 3753527 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357202.293675 3753527 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357202.293676 3753527 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:40:02.296649: 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 00:00:1762357204.560146 3753527 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357205.958380 3753650 service.cc:152] XLA service 0x7bae9c002da0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357205.958411 3753650 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:40:05.992041: 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 00:00:1762357206.117118 3753650 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357208.353689 3753650 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0911 - loss: 3.6553
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[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.0969 - loss: 3.4997
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.0986 - loss: 3.4577
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.0987 - loss: 3.4562 - val_accuracy: 0.2257 - val_loss: 2.1197
Epoch 2/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1419 - loss: 2.4711 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1460 - loss: 2.4635
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[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1516 - loss: 2.4375
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1540 - loss: 2.4240
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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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Epoch 13/98

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Epoch 14/98

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[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7400
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[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.7412
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3407 - loss: 1.7419 - val_accuracy: 0.3607 - val_loss: 1.4923
Epoch 15/98

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[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3339 - loss: 1.7546 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.7519
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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3637 - loss: 1.6874 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.6856
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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.6893
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.6914
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3524 - loss: 1.6925 - val_accuracy: 0.4065 - val_loss: 1.4599
Epoch 21/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7182 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7073
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Epoch 22/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3475 - loss: 1.6915 - val_accuracy: 0.3688 - val_loss: 1.4823
Epoch 23/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3609 - loss: 1.7195 
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[1m171/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3572 - loss: 1.6939
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3567 - loss: 1.6916
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.6904
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3552 - loss: 1.6898 - val_accuracy: 0.3753 - val_loss: 1.4702

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 710ms/step
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 740us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 26: 41.1 [%]
F1-score capturado en la ejecución 26: 33.64 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:19[0m 916ms/step
[1m 61/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 837us/step  
[1m137/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 739us/step
[1m214/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 707us/step
[1m291/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 693us/step
[1m362/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 695us/step
[1m436/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 693us/step
[1m506/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 697us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 716us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 743us/step
[1m141/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 719us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.53 [%]
Global F1 score (validation) = 31.52 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[3.0044338e-03 5.7969061e-03 3.3004035e-03 ... 3.2938174e-03
  5.8597759e-03 5.7460129e-04]
 [2.9241426e-03 5.6530312e-03 3.3243506e-03 ... 3.1979270e-03
  4.3748319e-03 5.3856452e-04]
 [2.7896359e-03 5.3743469e-03 3.2130722e-03 ... 3.5013608e-03
  4.1366345e-03 4.8661046e-04]
 ...
 [5.5669978e-05 6.0480732e-05 4.8721489e-05 ... 1.9200402e-03
  1.5110843e-03 6.5555312e-03]
 [6.3234940e-04 6.5179897e-04 5.4692617e-04 ... 1.7927635e-02
  6.2069143e-03 1.5093364e-02]
 [7.2677131e-03 6.7672525e-03 7.1652443e-03 ... 2.4229725e-01
  7.5790859e-03 8.3423713e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.7 [%]
Global accuracy score (test) = 38.92 [%]
Global F1 score (train) = 35.6 [%]
Global F1 score (test) = 32.67 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.78      0.37       161
 CAMINAR CON MÓVIL O LIBRO       0.33      0.20      0.25       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.33      0.01      0.01       161
          DE PIE BARRIENDO       0.47      0.60      0.53       161
   DE PIE DOBLANDO TOALLAS       0.00      0.00      0.00       161
    DE PIE MOVIENDO LIBROS       0.23      0.58      0.33       161
          DE PIE USANDO PC       0.64      0.18      0.28       161
        FASE REPOSO CON K5       0.33      0.86      0.48       161
INCREMENTAL CICLOERGOMETRO       0.99      0.84      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.29      0.39      0.33       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.65      0.59       161
                    TROTAR       0.83      0.80      0.82       138

                  accuracy                           0.39      2392
                 macro avg       0.35      0.39      0.33      2392
              weighted avg       0.34      0.39      0.32      2392

2025-11-05 16:40:35.047623: 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 16:40:35.059068: 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 00:00:1762357235.072277 3756666 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357235.076465 3756666 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357235.086237 3756666 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357235.086257 3756666 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357235.086259 3756666 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357235.086260 3756666 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:40:35.089433: 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 00:00:1762357237.343232 3756666 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357238.788226 3756795 service.cc:152] XLA service 0x7bc37800a680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357238.788264 3756795 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:40:38.821450: 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 00:00:1762357238.945334 3756795 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357241.190103 3756795 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 2/98

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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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Epoch 8/98

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Epoch 9/98

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Epoch 10/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3025 - loss: 1.8397
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3025 - loss: 1.8265 - val_accuracy: 0.3615 - val_loss: 1.5136
Epoch 11/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3203 - loss: 1.8166 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3188 - loss: 1.8119
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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.8074
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Epoch 12/98

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[1m 37/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3142 - loss: 1.8181 
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Epoch 13/98

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Epoch 14/98

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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3173 - loss: 1.7350
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Epoch 18/98

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Epoch 19/98

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Epoch 20/98

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Epoch 21/98

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Epoch 22/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.6741 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.6870
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[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.6933
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.6938 - val_accuracy: 0.3755 - val_loss: 1.4651
Epoch 23/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 22ms/step - accuracy: 0.3125 - loss: 1.6955
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3696 - loss: 1.6758 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3654 - loss: 1.6762
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[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3576 - loss: 1.6808
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3557 - loss: 1.6823
[1m247/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3543 - loss: 1.6840
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3536 - loss: 1.6848 - val_accuracy: 0.3783 - val_loss: 1.4444
Epoch 24/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.6653 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.6816
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[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.6906
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.6901
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Epoch 25/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.6870 
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Epoch 26/98

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Epoch 27/98

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Epoch 28/98

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[1m219/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.6687
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3546 - loss: 1.6692 - val_accuracy: 0.3680 - val_loss: 1.4733
Epoch 29/98

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[1m 38/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3683 - loss: 1.6821 
[1m 75/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3636 - loss: 1.6887
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[1m193/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3591 - loss: 1.6801
[1m235/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.6771
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3585 - loss: 1.6753 - val_accuracy: 0.3909 - val_loss: 1.4430
Epoch 30/98

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[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3644 - loss: 1.6476 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3681 - loss: 1.6381
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Epoch 31/98

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[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3570 - loss: 1.6492
[1m246/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3578 - loss: 1.6500
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3583 - loss: 1.6505 - val_accuracy: 0.3895 - val_loss: 1.4567
Epoch 32/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3594 - loss: 1.7524
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Epoch 33/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2656 - loss: 1.6487
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[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3689 - loss: 1.6391
[1m241/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3681 - loss: 1.6415
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3675 - loss: 1.6429 - val_accuracy: 0.3800 - val_loss: 1.4414
Epoch 34/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.4219 - loss: 1.4598
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3850 - loss: 1.6283 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3812 - loss: 1.6310
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[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3765 - loss: 1.6321
[1m205/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3746 - loss: 1.6329
[1m241/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3732 - loss: 1.6341
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3721 - loss: 1.6352 - val_accuracy: 0.4002 - val_loss: 1.4515
Epoch 35/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3906 - loss: 1.5424
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3814 - loss: 1.6127 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3735 - loss: 1.6200
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3704 - loss: 1.6264
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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3665 - loss: 1.6352
[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3654 - loss: 1.6373
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3647 - loss: 1.6381 - val_accuracy: 0.4225 - val_loss: 1.4358

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 691ms/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 841us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 27: 38.92 [%]
F1-score capturado en la ejecución 27: 32.67 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 770us/step
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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 66/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 769us/step
[1m139/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 728us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 42.25 [%]
Global F1 score (validation) = 35.89 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[3.4346287e-03 4.3176613e-03 3.5227528e-03 ... 2.1252343e-03
  3.8154752e-03 3.3668318e-04]
 [2.9869073e-03 3.8224719e-03 3.2090126e-03 ... 3.4635300e-03
  2.4686360e-03 5.5796839e-04]
 [3.1858790e-03 4.0666992e-03 3.4492272e-03 ... 3.8736514e-03
  2.3246929e-03 6.1550579e-04]
 ...
 [2.0312085e-05 2.1282172e-05 1.4612273e-05 ... 9.7236619e-04
  1.2785232e-03 5.1320544e-03]
 [3.1085525e-04 3.4891415e-04 2.5508512e-04 ... 8.9732436e-03
  6.3581029e-03 1.0538968e-02]
 [5.1536742e-03 6.4701559e-03 6.3043013e-03 ... 2.2099622e-01
  5.8410512e-03 5.7026478e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 46.42 [%]
Global accuracy score (test) = 45.4 [%]
Global F1 score (train) = 39.91 [%]
Global F1 score (test) = 38.2 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.95      0.36       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.48      0.65      0.55       161
   DE PIE DOBLANDO TOALLAS       0.31      0.29      0.30       161
    DE PIE MOVIENDO LIBROS       0.27      0.08      0.12       161
          DE PIE USANDO PC       0.62      0.93      0.74       161
        FASE REPOSO CON K5       0.90      0.71      0.79       161
INCREMENTAL CICLOERGOMETRO       0.99      0.89      0.94       161
           SENTADO LEYENDO       0.32      1.00      0.49       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.61      0.56      0.58       161
                    TROTAR       0.90      0.81      0.85       138

                  accuracy                           0.45      2392
                 macro avg       0.37      0.46      0.38      2392
              weighted avg       0.37      0.45      0.38      2392

2025-11-05 16:41:13.687811: 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 16:41:13.699315: 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 00:00:1762357273.712520 3760932 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357273.716688 3760932 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357273.726523 3760932 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357273.726549 3760932 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357273.726551 3760932 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357273.726553 3760932 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:41:13.729787: 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 00:00:1762357275.962673 3760932 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357277.395082 3761051 service.cc:152] XLA service 0x7abd30009350 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357277.395134 3761051 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:41:17.445073: 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 00:00:1762357277.572105 3761051 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357279.853140 3761051 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:39[0m 3s/step - accuracy: 0.0312 - loss: 4.1740
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[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1014 - loss: 3.6186
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[1m244/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1059 - loss: 3.4648
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.1077 - loss: 3.4163
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.1077 - loss: 3.4147 - val_accuracy: 0.2804 - val_loss: 2.1041
Epoch 2/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1717 - loss: 2.4902 
[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1724 - loss: 2.4609
[1m119/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1741 - loss: 2.4387
[1m154/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1752 - loss: 2.4250
[1m196/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1767 - loss: 2.4102
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Epoch 3/98

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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2938 - loss: 1.8704
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Epoch 8/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3025 - loss: 1.8433 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3012 - loss: 1.8488
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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3254 - loss: 1.7569 - val_accuracy: 0.3747 - val_loss: 1.4876
Epoch 13/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.6740 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3435 - loss: 1.6868
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[1m167/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.7070
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[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3368 - loss: 1.7169
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3367 - loss: 1.7192 - val_accuracy: 0.3648 - val_loss: 1.4928
Epoch 14/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.6682 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.6942
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Epoch 15/98

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Epoch 16/98

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Epoch 17/98

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Epoch 18/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7138
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3482 - loss: 1.7053 - val_accuracy: 0.4085 - val_loss: 1.4476
Epoch 19/98

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[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.6914 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7006
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[1m168/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3450 - loss: 1.7022
[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7007
[1m253/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.6993
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3482 - loss: 1.6985 - val_accuracy: 0.3743 - val_loss: 1.4583
Epoch 20/98

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[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.7015 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.6920
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Epoch 21/98

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[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3714 - loss: 1.6443 
[1m 83/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3676 - loss: 1.6582
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3605 - loss: 1.6692 - val_accuracy: 0.3844 - val_loss: 1.4647
Epoch 22/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.3438 - loss: 1.6440
[1m 45/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.6828 
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[1m164/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.6856
[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.6838
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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3472 - loss: 1.6821 - val_accuracy: 0.4192 - val_loss: 1.4481
Epoch 23/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.3750 - loss: 1.6454
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3618 - loss: 1.6721 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3598 - loss: 1.6726
[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.6700
[1m170/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.6707
[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3576 - loss: 1.6712
[1m250/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.6708
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3572 - loss: 1.6706 - val_accuracy: 0.3601 - val_loss: 1.4710
Epoch 24/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3594 - loss: 1.5778
[1m 36/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3543 - loss: 1.6564 
[1m 82/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3571 - loss: 1.6608
[1m124/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3581 - loss: 1.6627
[1m168/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3581 - loss: 1.6655
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3576 - loss: 1.6664
[1m255/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3570 - loss: 1.6668
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3568 - loss: 1.6669 - val_accuracy: 0.3887 - val_loss: 1.4562
Epoch 25/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8139
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3704 - loss: 1.6910 
[1m 78/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3680 - loss: 1.6755
[1m117/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3669 - loss: 1.6686
[1m159/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3657 - loss: 1.6660
[1m197/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3645 - loss: 1.6649
[1m237/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3636 - loss: 1.6640
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3630 - loss: 1.6631 - val_accuracy: 0.3761 - val_loss: 1.4682

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 705ms/step
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 712us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 45.4 [%]
F1-score capturado en la ejecución 28: 38.2 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m147/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 690us/step
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 743us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 72/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 705us/step
[1m141/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 717us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.61 [%]
Global F1 score (validation) = 32.31 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[2.4516943e-03 3.8176032e-03 2.5211649e-03 ... 2.1804781e-03
  7.1548433e-03 1.6822599e-04]
 [2.2989397e-03 3.5056656e-03 2.5277827e-03 ... 2.0981953e-03
  4.1640056e-03 1.6083999e-04]
 [2.0691880e-03 3.0757980e-03 2.2393132e-03 ... 2.7203478e-03
  3.0394159e-03 1.7874630e-04]
 ...
 [1.5858498e-04 1.5998966e-04 1.3313490e-04 ... 3.7762120e-03
  2.6894505e-03 9.0115461e-03]
 [5.6307195e-05 5.6469598e-05 4.6373258e-05 ... 1.3063691e-03
  1.3358638e-03 6.5903594e-03]
 [6.3716429e-03 6.0186437e-03 6.0164561e-03 ... 2.3764640e-01
  6.3218125e-03 6.2228632e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 43.97 [%]
Global accuracy score (test) = 42.89 [%]
Global F1 score (train) = 38.02 [%]
Global F1 score (test) = 36.51 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.27      0.04      0.07       161
       CAMINAR USUAL SPEED       0.25      0.83      0.38       161
            CAMINAR ZIGZAG       0.15      0.12      0.13       161
          DE PIE BARRIENDO       0.53      0.73      0.62       161
   DE PIE DOBLANDO TOALLAS       0.40      0.55      0.47       161
    DE PIE MOVIENDO LIBROS       0.00      0.00      0.00       161
          DE PIE USANDO PC       0.66      0.90      0.76       161
        FASE REPOSO CON K5       0.29      0.87      0.44       161
INCREMENTAL CICLOERGOMETRO       1.00      0.81      0.89       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.00      0.00      0.00       161
      SENTADO VIENDO LA TV       0.35      0.29      0.32       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.57      0.58       161
                    TROTAR       0.88      0.78      0.83       138

                  accuracy                           0.43      2392
                 macro avg       0.36      0.43      0.37      2392
              weighted avg       0.35      0.43      0.36      2392

2025-11-05 16:41:47.435384: 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 16:41:47.447419: 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 00:00:1762357307.460944 3764244 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357307.465002 3764244 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357307.475004 3764244 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357307.475023 3764244 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357307.475025 3764244 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357307.475026 3764244 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:41:47.478186: 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 00:00:1762357309.750861 3764244 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..
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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762357311.139117 3764375 service.cc:152] XLA service 0x799cdc01c2f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762357311.139148 3764375 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 16:41:51.173409: 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 00:00:1762357311.293495 3764375 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762357313.548255 3764375 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:19[0m 3s/step - accuracy: 0.0312 - loss: 4.6282
[1m 42/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0812 - loss: 4.0663  
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0881 - loss: 3.9157
[1m125/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0926 - loss: 3.7809
[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0951 - loss: 3.6835
[1m205/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0978 - loss: 3.5998
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1007 - loss: 3.5189
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.1024 - loss: 3.4770
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.1025 - loss: 3.4754 - val_accuracy: 0.2650 - val_loss: 2.0902
Epoch 2/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1710 - loss: 2.4553 
[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1707 - loss: 2.4438
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1713 - loss: 2.4317
[1m165/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1729 - loss: 2.4145
[1m204/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1740 - loss: 2.4016
[1m244/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1750 - loss: 2.3904
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1761 - loss: 2.3817 - val_accuracy: 0.3470 - val_loss: 1.8143
Epoch 3/98

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[1m 40/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2279 - loss: 2.0940 
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Epoch 4/98

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Epoch 5/98

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Epoch 6/98

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Epoch 7/98

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[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2903 - loss: 1.8425
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Epoch 8/98

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[1m 45/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.8074 
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Epoch 9/98

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Epoch 10/98

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Epoch 11/98

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Epoch 12/98

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[1m 84/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.7648
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[1m242/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3267 - loss: 1.7712
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Epoch 13/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7584
[1m 41/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3257 - loss: 1.7589 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3269 - loss: 1.7563
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[1m208/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.7553
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 1.7554
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3292 - loss: 1.7558 - val_accuracy: 0.3759 - val_loss: 1.4778
Epoch 14/98

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[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3611 - loss: 1.7368 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.7460
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[1m166/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7578
[1m207/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.7595
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3347 - loss: 1.7599
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Epoch 15/98

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[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3350 - loss: 1.7439 - val_accuracy: 0.3891 - val_loss: 1.4789
Epoch 16/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3594 - loss: 1.6732
[1m 43/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3324 - loss: 1.7226 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3327 - loss: 1.7219
[1m129/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.7260
[1m173/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3329 - loss: 1.7271
[1m216/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3334 - loss: 1.7280
[1m256/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3336 - loss: 1.7289
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3335 - loss: 1.7291 - val_accuracy: 0.3976 - val_loss: 1.4806
Epoch 17/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3750 - loss: 1.8280
[1m 39/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.7577 
[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7489
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7488
[1m163/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7462
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7436
[1m239/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7413
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3429 - loss: 1.7394 - val_accuracy: 0.3953 - val_loss: 1.4997
Epoch 18/98

[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3594 - loss: 1.6925
[1m 44/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3368 - loss: 1.7056 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3339 - loss: 1.7079
[1m121/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3323 - loss: 1.7091
[1m160/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 1.7098
[1m203/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.7103
[1m245/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3340 - loss: 1.7116
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3341 - loss: 1.7123 - val_accuracy: 0.3656 - val_loss: 1.4884

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 713ms/step
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 759us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 42.89 [%]
F1-score capturado en la ejecución 29: 36.51 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 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, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 61,647 (240.81 KB)
 Trainable params: 61,647 (240.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:00[0m 879ms/step
[1m 67/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 759us/step  
[1m136/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 743us/step
[1m211/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 717us/step
[1m282/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 714us/step
[1m348/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 725us/step
[1m424/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 714us/step
[1m496/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 711us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 751us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 67/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 762us/step
[1m139/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 730us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 36.56 [%]
Global F1 score (validation) = 31.86 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.00634365 0.00627718 0.00457386 ... 0.00374789 0.00974364 0.00044549]
 [0.0033325  0.00344555 0.00264369 ... 0.00480625 0.00418038 0.00039904]
 [0.00339916 0.00351244 0.00271387 ... 0.00475613 0.00419802 0.00039301]
 ...
 [0.0024446  0.00229491 0.00247498 ... 0.0621201  0.01074881 0.02780702]
 [0.00076345 0.0006857  0.00070089 ... 0.01518862 0.00565076 0.01225117]
 [0.0062065  0.00708705 0.00808254 ... 0.24708654 0.00682614 0.0083502 ]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 41.2 [%]
Global accuracy score (test) = 40.47 [%]
Global F1 score (train) = 36.79 [%]
Global F1 score (test) = 34.92 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       161
 CAMINAR CON MÓVIL O LIBRO       0.28      0.60      0.38       161
       CAMINAR USUAL SPEED       0.37      0.37      0.37       161
            CAMINAR ZIGZAG       0.20      0.19      0.19       161
          DE PIE BARRIENDO       0.44      0.62      0.52       161
   DE PIE DOBLANDO TOALLAS       0.24      0.19      0.21       161
    DE PIE MOVIENDO LIBROS       0.05      0.01      0.01       161
          DE PIE USANDO PC       0.49      0.92      0.64       161
        FASE REPOSO CON K5       0.00      0.00      0.00       161
INCREMENTAL CICLOERGOMETRO       1.00      0.84      0.91       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.33      0.14      0.20       161
      SENTADO VIENDO LA TV       0.24      0.86      0.38       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.60      0.59       161
                    TROTAR       0.87      0.80      0.83       138

                  accuracy                           0.40      2392
                 macro avg       0.34      0.41      0.35      2392
              weighted avg       0.34      0.40      0.34      2392


Accuracy capturado en la ejecución 30: 40.47 [%]
F1-score capturado en la ejecución 30: 34.92 [%]

=== RESUMEN FINAL ===
Accuracies: [39.55, 38.59, 43.44, 39.13, 40.51, 38.75, 45.44, 41.81, 40.38, 44.82, 45.07, 40.47, 41.18, 39.13, 39.26, 45.03, 43.9, 43.52, 41.93, 39.67, 43.73, 40.26, 38.63, 40.8, 44.98, 41.1, 38.92, 45.4, 42.89, 40.47]
F1-scores: [31.49, 32.03, 38.22, 33.2, 31.45, 31.34, 40.56, 35.36, 35.96, 39.72, 41.56, 33.9, 35.65, 31.53, 33.99, 38.41, 37.64, 36.12, 33.99, 31.43, 36.09, 36.01, 31.77, 32.75, 39.32, 33.64, 32.67, 38.2, 36.51, 34.92]
Accuracy mean: 41.6253 | std: 2.3120
F1 mean: 35.1810 | std: 2.9525

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_ESANN_acc_17_classes/metrics_test.npz
