2025-11-05 16:52:14.022665: 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 16:52:14.033883: 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 00:00:1762357934.047262 3774011 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762357934.051421 3774011 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762357934.061356 3774011 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357934.061375 3774011 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357934.061378 3774011 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762357934.061379 3774011 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 16:52:14.064553: 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 16:52:17,095	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-05 16:52:17,791	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-05 16:52:17,868	INFO trial.py:182 -- Creating a new dirname dir_67ffe_cd4f because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,871	INFO trial.py:182 -- Creating a new dirname dir_67ffe_7659 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,874	INFO trial.py:182 -- Creating a new dirname dir_67ffe_bd09 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,877	INFO trial.py:182 -- Creating a new dirname dir_67ffe_c5ce because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,880	INFO trial.py:182 -- Creating a new dirname dir_67ffe_2bc1 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,882	INFO trial.py:182 -- Creating a new dirname dir_67ffe_f912 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,884	INFO trial.py:182 -- Creating a new dirname dir_67ffe_98fd because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,886	INFO trial.py:182 -- Creating a new dirname dir_67ffe_c153 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,889	INFO trial.py:182 -- Creating a new dirname dir_67ffe_a86d because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,891	INFO trial.py:182 -- Creating a new dirname dir_67ffe_05db because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,895	INFO trial.py:182 -- Creating a new dirname dir_67ffe_7073 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,899	INFO trial.py:182 -- Creating a new dirname dir_67ffe_a6c5 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,902	INFO trial.py:182 -- Creating a new dirname dir_67ffe_d0e3 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,905	INFO trial.py:182 -- Creating a new dirname dir_67ffe_2823 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,908	INFO trial.py:182 -- Creating a new dirname dir_67ffe_e92d because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,912	INFO trial.py:182 -- Creating a new dirname dir_67ffe_3f92 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,915	INFO trial.py:182 -- Creating a new dirname dir_67ffe_6510 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,922	INFO trial.py:182 -- Creating a new dirname dir_67ffe_fd08 because trial dirname 'dir_67ffe' already exists.
2025-11-05 16:52:17,926	INFO trial.py:182 -- Creating a new dirname dir_67ffe_8006 because trial dirname 'dir_67ffe' 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_M/case_M_ESANN_acc_17_classes/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-05_16-52-16_361460_3774011/artifacts/2025-11-05_16-52-17/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-05 16:52:18. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_67ffe    PENDING            2   adam            tanh                                   64                 32                  5          3.37127e-05        119 │
│ trial_67ffe    PENDING            2   rmsprop         tanh                                   32                128                  3          0.00290294         146 │
│ trial_67ffe    PENDING            3   adam            relu                                  128                128                  5          9.67942e-05         85 │
│ trial_67ffe    PENDING            2   adam            tanh                                  128                128                  3          0.000479242         97 │
│ trial_67ffe    PENDING            4   adam            tanh                                   64                128                  3          5.0029e-05         137 │
│ trial_67ffe    PENDING            2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120 │
│ trial_67ffe    PENDING            2   rmsprop         tanh                                   32                 32                  3          0.00128622         111 │
│ trial_67ffe    PENDING            4   rmsprop         tanh                                   64                 64                  3          0.000980258        118 │
│ trial_67ffe    PENDING            3   adam            relu                                   32                 64                  3          0.000199406        132 │
│ trial_67ffe    PENDING            2   adam            tanh                                   64                 32                  5          0.000685751        145 │
│ trial_67ffe    PENDING            2   adam            tanh                                   64                128                  3          7.12175e-05         81 │
│ trial_67ffe    PENDING            4   rmsprop         tanh                                   32                 64                  5          0.000629084         74 │
│ trial_67ffe    PENDING            2   rmsprop         relu                                   64                 32                  5          0.000408657         83 │
│ trial_67ffe    PENDING            2   adam            tanh                                   32                 64                  3          0.00186374          89 │
│ trial_67ffe    PENDING            2   rmsprop         tanh                                  128                 64                  5          0.000376048         62 │
│ trial_67ffe    PENDING            3   adam            tanh                                   32                 64                  5          2.41462e-05         75 │
│ trial_67ffe    PENDING            2   adam            relu                                  128                 32                  5          1.95135e-05         70 │
│ trial_67ffe    PENDING            4   rmsprop         tanh                                   32                 32                  5          0.000767441         57 │
│ trial_67ffe    PENDING            2   adam            tanh                                   32                 64                  3          0.000699197         84 │
│ trial_67ffe    PENDING            3   rmsprop         relu                                  128                 32                  5          0.0014412           83 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           120 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           111 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00129 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           118 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00098 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           145 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00069 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_67ffe config            │
├─────────────────────────────────────┤
│ N_capas                           3 │
│ epochs                           85 │
│ funcion_activacion             relu │
│ numero_filtros                  128 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                128 │
│ tasa_aprendizaje             0.0001 │
╰─────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           146 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje              0.0029 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            97 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00048 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            83 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00041 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            75 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           137 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_67ffe config            │
├─────────────────────────────────────┤
│ N_capas                           3 │
│ epochs                          132 │
│ funcion_activacion             relu │
│ numero_filtros                   64 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 32 │
│ tasa_aprendizaje             0.0002 │
╰─────────────────────────────────────╯
Trial trial_67ffe started with configuration:
[36m(train_cnn_ray_tune pid=3775654)[0m 2025-11-05 16:52:21.129852: 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=3775654)[0m 2025-11-05 16:52:21.150955: 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=3775654)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=3775654)[0m E0000 00:00:1762357941.180968 3776779 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=3775654)[0m E0000 00:00:1762357941.189579 3776779 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=3775654)[0m W0000 00:00:1762357941.210470 3776779 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=3775654)[0m W0000 00:00:1762357941.210516 3776779 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=3775654)[0m W0000 00:00:1762357941.210520 3776779 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=3775654)[0m W0000 00:00:1762357941.210523 3776779 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=3775654)[0m 2025-11-05 16:52:21.216652: 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=3775654)[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=3775652)[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=3775652)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=3775654)[0m 2025-11-05 16:52:24.391628: 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=3775654)[0m 2025-11-05 16:52:24.391680: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=3775654)[0m 2025-11-05 16:52:24.391688: 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=3775654)[0m 2025-11-05 16:52:24.391694: 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=3775654)[0m 2025-11-05 16:52:24.391700: 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=3775654)[0m 2025-11-05 16:52:24.391703: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=3775654)[0m 2025-11-05 16:52:24.391961: 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=3775654)[0m 2025-11-05 16:52:24.392000: 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=3775654)[0m 2025-11-05 16:52:24.392005: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭─────────────────────────────────────╮
│ Trial trial_67ffe config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                           84 │
│ funcion_activacion             tanh │
│ numero_filtros                   64 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 32 │
│ tasa_aprendizaje             0.0007 │
╰─────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            57 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00077 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            89 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00186 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           119 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            81 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            74 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00063 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            70 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            62 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00038 │
╰──────────────────────────────────────╯
Trial trial_67ffe started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_67ffe config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            83 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00144 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775652)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=3775652)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=3775652)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=3775652)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=3775652)[0m │ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
[36m(train_cnn_ray_tune pid=3775652)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3775652)[0m │ layer_normalization             │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=3775652)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3775652)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3775652)[0m │ dropout (Dropout)               │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=3775652)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3775652)[0m │ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
[36m(train_cnn_ray_tune pid=3775652)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3775652)[0m │ layer_normalization_1           │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=3775652)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3775652)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3775652)[0m │ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=3775652)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3775652)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=3775652)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=3775652)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3775652)[0m │ dropout_2 (Dropout)             │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=3775652)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3775652)[0m │ dense (Dense)                   │ (None, 15)             │           495 │
[36m(train_cnn_ray_tune pid=3775652)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=3775652)[0m  Total params: 27,759 (108.43 KB)
[36m(train_cnn_ray_tune pid=3775652)[0m  Trainable params: 27,759 (108.43 KB)
[36m(train_cnn_ray_tune pid=3775652)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=3775652)[0m Epoch 1/111
[36m(train_cnn_ray_tune pid=3775650)[0m  Total params: 326,927 (1.25 MB)
[36m(train_cnn_ray_tune pid=3775650)[0m  Trainable params: 326,927 (1.25 MB)
[36m(train_cnn_ray_tune pid=3775652)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22:47[0m 2s/step - accuracy: 0.0312 - loss: 4.5698
[36m(train_cnn_ray_tune pid=3775652)[0m 
[1m  4/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 18ms/step - accuracy: 0.0378 - loss: 4.2242
[1m  8/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 16ms/step - accuracy: 0.0564 - loss: 4.1151 
[36m(train_cnn_ray_tune pid=3775654)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:29[0m 2s/step - accuracy: 0.0312 - loss: 4.5380
[1m  6/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 13ms/step - accuracy: 0.0575 - loss: 4.2870 
[36m(train_cnn_ray_tune pid=3775652)[0m 
[1m 12/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 15ms/step - accuracy: 0.0677 - loss: 4.0551
[1m 17/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 14ms/step - accuracy: 0.0724 - loss: 4.0087
[36m(train_cnn_ray_tune pid=3775652)[0m 
[1m 21/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 14ms/step - accuracy: 0.0747 - loss: 3.9888
[36m(train_cnn_ray_tune pid=3775659)[0m 
[1m  3/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.0712 - loss: 4.0784 
[1m  6/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.0744 - loss: 3.9788
[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m  4/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 24ms/step - accuracy: 0.0234 - loss: 4.7413    
[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m  7/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.0303 - loss: 4.5795
[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m  9/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.0330 - loss: 4.5177
[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m 12/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 25ms/step - accuracy: 0.0368 - loss: 4.4757
[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m 16/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0433 - loss: 4.4286
[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m 25/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0516 - loss: 4.3937
[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m 27/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0532 - loss: 4.3868
[36m(train_cnn_ray_tune pid=3775649)[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=3775649)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │[32m [repeated 100x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 188x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m │ layer_normalization             │ (None, 3, 32)          │            64 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 50x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m │ dropout (Dropout)               │ (None, 3, 32)          │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m │ dropout_4 (Dropout)             │ (None, 32)             │             0 │[32m [repeated 50x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m │ dense (Dense)                   │ (None, 15)             │           495 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m  Total params: 56,239 (219.68 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m  Trainable params: 56,239 (219.68 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775634)[0m Epoch 1/70[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
[1m 50/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 35ms/step - accuracy: 0.0661 - loss: 4.4678
[1m 52/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 34ms/step - accuracy: 0.0661 - loss: 4.4664
[1m 54/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 34ms/step - accuracy: 0.0661 - loss: 4.4648
[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29:52[0m 6s/step - accuracy: 0.0312 - loss: 4.6692[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775653)[0m 
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 33ms/step - accuracy: 0.0770 - loss: 3.7709
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 33ms/step - accuracy: 0.0769 - loss: 3.7663
[1m108/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 32ms/step - accuracy: 0.0768 - loss: 3.7618
[36m(train_cnn_ray_tune pid=3775649)[0m 
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 33ms/step - accuracy: 0.0719 - loss: 4.0881
[1m 80/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 33ms/step - accuracy: 0.0718 - loss: 4.0848[32m [repeated 91x across cluster][0m
[36m(train_cnn_ray_tune pid=3775658)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m44:18[0m 5s/step - accuracy: 0.0625 - loss: 3.5315
[1m  3/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 39ms/step - accuracy: 0.0729 - loss: 3.9299[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775652)[0m 
[1m275/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.0803 - loss: 3.6034
[1m277/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.0803 - loss: 3.6015[32m [repeated 271x across cluster][0m
[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m Epoch 2/83[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m Epoch 3/70[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 3/97[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 16:52:48. 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_67ffe    RUNNING            2   adam            tanh                                   64                 32                  5          3.37127e-05        119 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00290294         146 │
│ trial_67ffe    RUNNING            3   adam            relu                                  128                128                  5          9.67942e-05         85 │
│ trial_67ffe    RUNNING            2   adam            tanh                                  128                128                  3          0.000479242         97 │
│ trial_67ffe    RUNNING            4   adam            tanh                                   64                128                  3          5.0029e-05         137 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   32                 32                  3          0.00128622         111 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   64                 64                  3          0.000980258        118 │
│ trial_67ffe    RUNNING            3   adam            relu                                   32                 64                  3          0.000199406        132 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   64                 32                  5          0.000685751        145 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   64                128                  3          7.12175e-05         81 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   32                 64                  5          0.000629084         74 │
│ trial_67ffe    RUNNING            2   rmsprop         relu                                   64                 32                  5          0.000408657         83 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   32                 64                  3          0.00186374          89 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                  128                 64                  5          0.000376048         62 │
│ trial_67ffe    RUNNING            3   adam            tanh                                   32                 64                  5          2.41462e-05         75 │
│ trial_67ffe    RUNNING            2   adam            relu                                  128                 32                  5          1.95135e-05         70 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   32                 32                  5          0.000767441         57 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   32                 64                  3          0.000699197         84 │
│ trial_67ffe    RUNNING            3   rmsprop         relu                                  128                 32                  5          0.0014412           83 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m Epoch 5/70[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m Epoch 3/81[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m Epoch 7/70[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 3/137[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m Epoch 3/75[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 16:53:18. 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_67ffe    RUNNING            2   adam            tanh                                   64                 32                  5          3.37127e-05        119 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00290294         146 │
│ trial_67ffe    RUNNING            3   adam            relu                                  128                128                  5          9.67942e-05         85 │
│ trial_67ffe    RUNNING            2   adam            tanh                                  128                128                  3          0.000479242         97 │
│ trial_67ffe    RUNNING            4   adam            tanh                                   64                128                  3          5.0029e-05         137 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   32                 32                  3          0.00128622         111 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   64                 64                  3          0.000980258        118 │
│ trial_67ffe    RUNNING            3   adam            relu                                   32                 64                  3          0.000199406        132 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   64                 32                  5          0.000685751        145 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   64                128                  3          7.12175e-05         81 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   32                 64                  5          0.000629084         74 │
│ trial_67ffe    RUNNING            2   rmsprop         relu                                   64                 32                  5          0.000408657         83 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   32                 64                  3          0.00186374          89 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                  128                 64                  5          0.000376048         62 │
│ trial_67ffe    RUNNING            3   adam            tanh                                   32                 64                  5          2.41462e-05         75 │
│ trial_67ffe    RUNNING            2   adam            relu                                  128                 32                  5          1.95135e-05         70 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   32                 32                  5          0.000767441         57 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   32                 64                  3          0.000699197         84 │
│ trial_67ffe    RUNNING            3   rmsprop         relu                                  128                 32                  5          0.0014412           83 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m Epoch 3/74[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 11/62[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m Epoch 9/120[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m Epoch 6/118[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m Epoch 13/83[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m Epoch 11/120[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 16:53:48. 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_67ffe    RUNNING            2   adam            tanh                                   64                 32                  5          3.37127e-05        119 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00290294         146 │
│ trial_67ffe    RUNNING            3   adam            relu                                  128                128                  5          9.67942e-05         85 │
│ trial_67ffe    RUNNING            2   adam            tanh                                  128                128                  3          0.000479242         97 │
│ trial_67ffe    RUNNING            4   adam            tanh                                   64                128                  3          5.0029e-05         137 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   32                 32                  3          0.00128622         111 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   64                 64                  3          0.000980258        118 │
│ trial_67ffe    RUNNING            3   adam            relu                                   32                 64                  3          0.000199406        132 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   64                 32                  5          0.000685751        145 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   64                128                  3          7.12175e-05         81 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   32                 64                  5          0.000629084         74 │
│ trial_67ffe    RUNNING            2   rmsprop         relu                                   64                 32                  5          0.000408657         83 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   32                 64                  3          0.00186374          89 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                  128                 64                  5          0.000376048         62 │
│ trial_67ffe    RUNNING            3   adam            tanh                                   32                 64                  5          2.41462e-05         75 │
│ trial_67ffe    RUNNING            2   adam            relu                                  128                 32                  5          1.95135e-05         70 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   32                 32                  5          0.000767441         57 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   32                 64                  3          0.000699197         84 │
│ trial_67ffe    RUNNING            3   rmsprop         relu                                  128                 32                  5          0.0014412           83 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m Epoch 6/84[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m Epoch 8/81[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m Epoch 5/57[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m Epoch 7/84[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 19/62[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 20/62[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 16:54:18. 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_67ffe    RUNNING            2   adam            tanh                                   64                 32                  5          3.37127e-05        119 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00290294         146 │
│ trial_67ffe    RUNNING            3   adam            relu                                  128                128                  5          9.67942e-05         85 │
│ trial_67ffe    RUNNING            2   adam            tanh                                  128                128                  3          0.000479242         97 │
│ trial_67ffe    RUNNING            4   adam            tanh                                   64                128                  3          5.0029e-05         137 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                   32                 32                  3          0.00128622         111 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   64                 64                  3          0.000980258        118 │
│ trial_67ffe    RUNNING            3   adam            relu                                   32                 64                  3          0.000199406        132 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   64                 32                  5          0.000685751        145 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   64                128                  3          7.12175e-05         81 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   32                 64                  5          0.000629084         74 │
│ trial_67ffe    RUNNING            2   rmsprop         relu                                   64                 32                  5          0.000408657         83 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   32                 64                  3          0.00186374          89 │
│ trial_67ffe    RUNNING            2   rmsprop         tanh                                  128                 64                  5          0.000376048         62 │
│ trial_67ffe    RUNNING            3   adam            tanh                                   32                 64                  5          2.41462e-05         75 │
│ trial_67ffe    RUNNING            2   adam            relu                                  128                 32                  5          1.95135e-05         70 │
│ trial_67ffe    RUNNING            4   rmsprop         tanh                                   32                 32                  5          0.000767441         57 │
│ trial_67ffe    RUNNING            2   adam            tanh                                   32                 64                  3          0.000699197         84 │
│ trial_67ffe    RUNNING            3   rmsprop         relu                                  128                 32                  5          0.0014412           83 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m Epoch 7/146[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m Epoch 25/70[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[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=3775634)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775649)[0m 2025-11-05 16:52:21.386246: 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=3775649)[0m 2025-11-05 16:52:21.407852: 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=3775662)[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=3775662)[0m E0000 00:00:1762357941.448873 3776885 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=3775662)[0m E0000 00:00:1762357941.457883 3776885 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=3775649)[0m W0000 00:00:1762357941.463779 3776883 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=3775649)[0m 2025-11-05 16:52:21.469776: 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=3775649)[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=3775636)[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=3775636)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m 2025-11-05 16:52:24.794448: 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=3775649)[0m 2025-11-05 16:52:24.794512: 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=3775649)[0m 2025-11-05 16:52:24.794523: 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=3775649)[0m 2025-11-05 16:52:24.794530: 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=3775649)[0m 2025-11-05 16:52:24.794535: 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=3775649)[0m 2025-11-05 16:52:24.794539: 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=3775649)[0m 2025-11-05 16:52:24.794810: 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=3775649)[0m 2025-11-05 16:52:24.794847: 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=3775649)[0m 2025-11-05 16:52:24.794853: 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=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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[36m(train_cnn_ray_tune pid=3775634)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:54:35. Total running time: 2min 17s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             134.154 │
│ time_total_s                 134.154 │
│ training_iteration                 1 │
│ val_accuracy                 0.21779 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:54:35. Total running time: 2min 17s
[36m(train_cnn_ray_tune pid=3775650)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:54:37. Total running time: 2min 19s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             136.981 │
│ time_total_s                 136.981 │
│ training_iteration                 1 │
│ val_accuracy                 0.20389 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:54:37. Total running time: 2min 19s
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m Epoch 18/119[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-05 16:54:48. Total running time: 2min 30s
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_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          3.37127e-05        119                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00290294         146                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                 32                  3          0.00128622         111                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   64                 64                  3          0.000980258        118                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.000408657         83                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                  128                 64                  5          0.000376048         62                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 32                  5          0.000767441         57                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.0014412           83                                              │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m Epoch 19/83[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m Epoch 9/146[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 29/62[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 30/62[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[1m  3/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 35ms/step - accuracy: 0.0755 - loss: 3.4631 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3775658)[0m 
[1m113/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m11s[0m 24ms/step - accuracy: 0.1291 - loss: 2.6487[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 31/62[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775659)[0m 
[1m238/289[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.1617 - loss: 2.5207
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m Epoch 12/89[32m [repeated 10x across cluster][0m
Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-05 16:55:18. 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_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          3.37127e-05        119                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00290294         146                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                 32                  3          0.00128622         111                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   64                 64                  3          0.000980258        118                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.000408657         83                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                  128                 64                  5          0.000376048         62                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 32                  5          0.000767441         57                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.0014412           83                                              │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m Epoch 33/83[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m Epoch 26/120[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 26/97[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m Epoch 14/89[32m [repeated 13x across cluster][0m
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[36m(train_cnn_ray_tune pid=3775652)[0m 
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[36m(train_cnn_ray_tune pid=3775652)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:55:43. Total running time: 3min 26s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              203.26 │
│ time_total_s                  203.26 │
│ training_iteration                 1 │
│ val_accuracy                 0.19992 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:55:43. Total running time: 3min 26s
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 39/62[32m [repeated 8x across cluster][0m

Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-05 16:55:48. 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_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          3.37127e-05        119                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00290294         146                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   64                 64                  3          0.000980258        118                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.000408657         83                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                  128                 64                  5          0.000376048         62                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 32                  5          0.000767441         57                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.0014412           83                                              │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 32ms/step - accuracy: 0.1832 - loss: 2.4116 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m Epoch 10/74[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m Epoch 29/83[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
[1m 45/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.1824 - loss: 2.4387[32m [repeated 211x across cluster][0m
[36m(train_cnn_ray_tune pid=3775659)[0m 
[1m289/289[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 22ms/step - accuracy: 0.1560 - loss: 2.5057 - val_accuracy: 0.2156 - val_loss: 2.3782[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775636)[0m 
[1m  4/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.1815 - loss: 2.4490 [32m [repeated 29x across cluster][0m
[36m(train_cnn_ray_tune pid=3775659)[0m Epoch 29/145[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m  3/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 44ms/step - accuracy: 0.1120 - loss: 3.1824 
[36m(train_cnn_ray_tune pid=3775651)[0m 
[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 83ms/step - accuracy: 0.1797 - loss: 2.3609
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 32ms/step - accuracy: 0.1814 - loss: 2.3712 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
[1m488/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.1869 - loss: 2.4391
[1m490/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.1868 - loss: 2.4391
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[36m(train_cnn_ray_tune pid=3775642)[0m 
[1m  4/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.1289 - loss: 2.8490 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m Epoch 30/145[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m Epoch 43/83[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 46/62[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775636)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-05 16:56:18. Total running time: 4min 0s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          3.37127e-05        119                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00290294         146                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   64                 64                  3          0.000980258        118                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.000408657         83                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                  128                 64                  5          0.000376048         62                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 32                  5          0.000767441         57                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.0014412           83                                              │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m Epoch 17/89[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m Epoch 23/81[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m Epoch 34/145[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775653)[0m 
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[1m  6/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1933 - loss: 2.4262[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m Epoch 36/119[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 53/62[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-05 16:56:48. Total running time: 4min 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_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          3.37127e-05        119                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00290294         146                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   64                 64                  3          0.000980258        118                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.000408657         83                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                  128                 64                  5          0.000376048         62                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 32                  5          0.000767441         57                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.0014412           83                                              │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m Epoch 54/62[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m Epoch 40/120[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775643)[0m Epoch 39/83[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[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=3775649)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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[36m(train_cnn_ray_tune pid=3775649)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:57:05. Total running time: 4min 47s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             284.598 │
│ time_total_s                 284.598 │
│ training_iteration                 1 │
│ val_accuracy                 0.19913 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:57:05. Total running time: 4min 47s
[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m Epoch 56/83[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m Epoch 57/83[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m Epoch 19/146[32m [repeated 10x across cluster][0m

Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-05 16:57:18. Total running time: 5min 0s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          3.37127e-05        119                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00290294         146                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   64                 64                  3          0.000980258        118                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.000408657         83                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                  128                 64                  5          0.000376048         62                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    RUNNING              3   rmsprop         relu                                  128                 32                  5          0.0014412           83                                              │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 31ms/step - accuracy: 0.2077 - loss: 2.3819[32m [repeated 216x across cluster][0m
[36m(train_cnn_ray_tune pid=3775643)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 45/97[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775653)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 254ms/step
[36m(train_cnn_ray_tune pid=3775653)[0m 
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[1m14/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
[1m78/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step
[1m85/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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[36m(train_cnn_ray_tune pid=3775653)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:57:26. Total running time: 5min 8s
[36m(train_cnn_ray_tune pid=3775653)[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=3775653)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             305.716 │
│ time_total_s                 305.716 │
│ training_iteration                 1 │
│ val_accuracy                 0.25511 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:57:26. Total running time: 5min 8s
[36m(train_cnn_ray_tune pid=3775657)[0m Epoch 27/118[32m [repeated 11x across cluster][0m

Trial trial_67ffe finished iteration 1 at 2025-11-05 16:57:27. Total running time: 5min 10s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             306.986 │
│ time_total_s                 306.986 │
│ training_iteration                 1 │
│ val_accuracy                 0.25749 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:57:27. Total running time: 5min 10s
[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:57:30. Total running time: 5min 12s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             309.492 │
│ time_total_s                 309.492 │
│ training_iteration                 1 │
│ val_accuracy                 0.25194 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:57:30. Total running time: 5min 12s
[36m(train_cnn_ray_tune pid=3775636)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3775642)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3775642)[0m 
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[36m(train_cnn_ray_tune pid=3775642)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:57:36. Total running time: 5min 19s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             315.866 │
│ time_total_s                 315.866 │
│ training_iteration                 1 │
│ val_accuracy                 0.18841 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:57:36. Total running time: 5min 19s
[36m(train_cnn_ray_tune pid=3775661)[0m Epoch 16/74[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m Epoch 29/118[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m Epoch 20/132[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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Trial status: 8 TERMINATED | 12 RUNNING
Current time: 2025-11-05 16:57:48. Total running time: 5min 30s
Logical resource usage: 12.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00290294         146                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   64                 64                  3          0.000980258        118                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[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=3775654)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775657)[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=3775657)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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[36m(train_cnn_ray_tune pid=3775654)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:57:49. Total running time: 5min 31s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              329.01 │
│ time_total_s                  329.01 │
│ training_iteration                 1 │
│ val_accuracy                 0.19357 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:57:49. Total running time: 5min 31s
[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:57:51. Total running time: 5min 33s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             330.596 │
│ time_total_s                 330.596 │
│ training_iteration                 1 │
│ val_accuracy                  0.2166 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:57:51. Total running time: 5min 33s
[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m Epoch 25/89[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775657)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m Epoch 23/146[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m Epoch 27/84[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m Epoch 21/75[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m Epoch 25/146[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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Trial status: 10 TERMINATED | 10 RUNNING
Current time: 2025-11-05 16:58:18. Total running time: 6min 0s
Logical resource usage: 10.0/20 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_67ffe    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00290294         146                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120        1            329.01          0.193568 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   64                 64                  3          0.000980258        118        1            330.596         0.216597 │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[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=3775635)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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[36m(train_cnn_ray_tune pid=3775635)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:58:21. Total running time: 6min 3s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             360.527 │
│ time_total_s                 360.527 │
│ training_iteration                 1 │
│ val_accuracy                 0.28708 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:58:21. Total running time: 6min 3s
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 63/97[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m Epoch 31/84[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 67/97[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 69/97[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m Epoch 63/145[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
[1m 80/289[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.1605 - loss: 2.4524[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=3775651)[0m 
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[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step - accuracy: 0.2237 - loss: 2.3053
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[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 8ms/step - accuracy: 0.1894 - loss: 2.3968
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 56ms/step - accuracy: 0.2344 - loss: 2.3501[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
[1m  6/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 13ms/step - accuracy: 0.1470 - loss: 2.4940 
[1m 11/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 12ms/step - accuracy: 0.1611 - loss: 2.4772[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3775658)[0m Epoch 28/132[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775659)[0m 
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Trial status: 11 TERMINATED | 9 RUNNING
Current time: 2025-11-05 16:58:48. Total running time: 6min 30s
Logical resource usage: 9.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                 32                  5          0.000685751        145                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00290294         146        1            360.527         0.287076 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120        1            329.01          0.193568 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   64                 64                  3          0.000980258        118        1            330.596         0.216597 │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[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=3775659)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775659)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m Epoch 27/75[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775659)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:58:53. Total running time: 6min 35s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             392.328 │
│ time_total_s                 392.328 │
│ training_iteration                 1 │
│ val_accuracy                 0.21481 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:58:53. Total running time: 6min 35s
[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 34/137[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 35/137[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 36/137[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 37/137[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 86/97[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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Trial status: 12 TERMINATED | 8 RUNNING
Current time: 2025-11-05 16:59:18. Total running time: 7min 1s
Logical resource usage: 8.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_67ffe    RUNNING              2   adam            tanh                                  128                128                  3          0.000479242         97                                              │
│ trial_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000629084         74                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.00186374          89                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00290294         146        1            360.527         0.287076 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120        1            329.01          0.193568 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   64                 64                  3          0.000980258        118        1            330.596         0.216597 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          0.000685751        145        1            392.328         0.21481  │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 39/137[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775662)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 91/97[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:59:30. Total running time: 7min 13s
[36m(train_cnn_ray_tune pid=3775662)[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=3775662)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775661)[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=3775661)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             429.864 │
│ time_total_s                 429.864 │
│ training_iteration                 1 │
│ val_accuracy                 0.24479 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:59:30. Total running time: 7min 13s
[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:59:31. Total running time: 7min 14s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             430.901 │
│ time_total_s                 430.901 │
│ training_iteration                 1 │
│ val_accuracy                 0.24816 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:59:31. Total running time: 7min 14s
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775661)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m Epoch 97/97[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[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=3775651)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775651)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 16:59:41. Total running time: 7min 23s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             440.549 │
│ time_total_s                 440.549 │
│ training_iteration                 1 │
│ val_accuracy                 0.31368 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 16:59:41. Total running time: 7min 23s
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m Epoch 63/81[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m Epoch 48/84[32m [repeated 9x across cluster][0m

Trial status: 15 TERMINATED | 5 RUNNING
Current time: 2025-11-05 16:59:48. Total running time: 7min 31s
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_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00290294         146        1            360.527         0.287076 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                  128                128                  3          0.000479242         97        1            440.549         0.313679 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120        1            329.01          0.193568 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   64                 64                  3          0.000980258        118        1            330.596         0.216597 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          0.000685751        145        1            392.328         0.21481  │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 64                  5          0.000629084         74        1            430.901         0.248164 │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   32                 64                  3          0.00186374          89        1            429.864         0.244789 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775656)[0m 
[1m558/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 6ms/step - accuracy: 0.1040 - loss: 2.7981[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m Epoch 38/75[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m 78/289[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 10ms/step - accuracy: 0.1407 - loss: 2.5999
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[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m 90/289[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.1408 - loss: 2.5998
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m Epoch 70/81[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m Epoch 53/84[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m 67/289[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.1593 - loss: 2.5710 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m Epoch 75/81[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[1m174/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 5ms/step - accuracy: 0.2112 - loss: 2.3592[32m [repeated 178x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 44ms/step - accuracy: 0.0938 - loss: 2.6744[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m 60/289[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 10ms/step - accuracy: 0.1534 - loss: 2.5635
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m507/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 5ms/step - accuracy: 0.2091 - loss: 2.3657
[1m518/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 5ms/step - accuracy: 0.2091 - loss: 2.3655
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m 78/289[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 10ms/step - accuracy: 0.1539 - loss: 2.5621
[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m 84/289[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.1539 - loss: 2.5620
[1m 90/289[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.1540 - loss: 2.5618 
[1m 96/289[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.1541 - loss: 2.5616
[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m289/289[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 11ms/step - accuracy: 0.1613 - loss: 2.5695 - val_accuracy: 0.2134 - val_loss: 2.3749[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775660)[0m 
[1m 10/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2406 - loss: 2.3857  
[1m 19/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2344 - loss: 2.3827
[36m(train_cnn_ray_tune pid=3775656)[0m Epoch 43/75[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
[1m  7/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.1266 - loss: 2.5806  
[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m366/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.2076 - loss: 2.3687[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=3775660)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 41ms/step - accuracy: 0.2031 - loss: 2.5278
[1m  9/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2237 - loss: 2.3809  [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775660)[0m 
[1m 17/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2250 - loss: 2.3738
[1m 25/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2261 - loss: 2.3671
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[36m(train_cnn_ray_tune pid=3775658)[0m 
[1m398/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 6ms/step - accuracy: 0.2239 - loss: 2.3250
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 7ms/step - accuracy: 0.1181 - loss: 2.7291 - val_accuracy: 0.2178 - val_loss: 2.4161[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775660)[0m 
[1m 10/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2246 - loss: 2.3232  
[1m 19/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2214 - loss: 2.3505[32m [repeated 2x across cluster][0m
Trial status: 15 TERMINATED | 5 RUNNING
Current time: 2025-11-05 17:00:18. Total running time: 8min 1s
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_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   64                128                  3          7.12175e-05         81                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00290294         146        1            360.527         0.287076 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                  128                128                  3          0.000479242         97        1            440.549         0.313679 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120        1            329.01          0.193568 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   64                 64                  3          0.000980258        118        1            330.596         0.216597 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          0.000685751        145        1            392.328         0.21481  │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 64                  5          0.000629084         74        1            430.901         0.248164 │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   32                 64                  3          0.00186374          89        1            429.864         0.244789 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775660)[0m Epoch 80/81[32m [repeated 10x across cluster][0m
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[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=3775660)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 57/137[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775660)[0m 
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[36m(train_cnn_ray_tune pid=3775660)[0m 
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step

Trial trial_67ffe finished iteration 1 at 2025-11-05 17:00:24. Total running time: 8min 6s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             483.274 │
│ time_total_s                 483.274 │
│ training_iteration                 1 │
│ val_accuracy                 0.27179 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 17:00:24. Total running time: 8min 6s
[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 59/137[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 63/137[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m Epoch 68/84[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-05 17:00:49. Total running time: 8min 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_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              3   adam            relu                                   32                 64                  3          0.000199406        132                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00290294         146        1            360.527         0.287076 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                  128                128                  3          0.000479242         97        1            440.549         0.313679 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120        1            329.01          0.193568 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   64                 64                  3          0.000980258        118        1            330.596         0.216597 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          0.000685751        145        1            392.328         0.21481  │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                128                  3          7.12175e-05         81        1            483.274         0.271789 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 64                  5          0.000629084         74        1            430.901         0.248164 │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   32                 64                  3          0.00186374          89        1            429.864         0.244789 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 67/137[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 69/137[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 71/137[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[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=3775658)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775658)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 17:01:07. Total running time: 8min 50s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             526.895 │
│ time_total_s                 526.895 │
│ training_iteration                 1 │
│ val_accuracy                 0.32519 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 17:01:07. Total running time: 8min 50s
[36m(train_cnn_ray_tune pid=3775658)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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Trial status: 17 TERMINATED | 3 RUNNING
Current time: 2025-11-05 17:01:19. Total running time: 9min 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_67ffe    RUNNING              4   adam            tanh                                   64                128                  3          5.0029e-05         137                                              │
│ trial_67ffe    RUNNING              3   adam            tanh                                   32                 64                  5          2.41462e-05         75                                              │
│ trial_67ffe    RUNNING              2   adam            tanh                                   32                 64                  3          0.000699197         84                                              │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00290294         146        1            360.527         0.287076 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                  128                128                  3          0.000479242         97        1            440.549         0.313679 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120        1            329.01          0.193568 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   64                 64                  3          0.000980258        118        1            330.596         0.216597 │
│ trial_67ffe    TERMINATED           3   adam            relu                                   32                 64                  3          0.000199406        132        1            526.895         0.325194 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          0.000685751        145        1            392.328         0.21481  │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                128                  3          7.12175e-05         81        1            483.274         0.271789 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 64                  5          0.000629084         74        1            430.901         0.248164 │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   32                 64                  3          0.00186374          89        1            429.864         0.244789 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3775633)[0m Epoch 83/84[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[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=3775633)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 131ms/step
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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[36m(train_cnn_ray_tune pid=3775633)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 17:01:23. Total running time: 9min 6s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             542.971 │
│ time_total_s                 542.971 │
│ training_iteration                 1 │
│ val_accuracy                 0.30336 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 17:01:23. Total running time: 9min 6s
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 81/137[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m Epoch 72/75[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[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=3775655)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m 
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[36m(train_cnn_ray_tune pid=3775655)[0m Epoch 92/137[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3775655)[0m 
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Trial trial_67ffe finished iteration 1 at 2025-11-05 17:01:47. Total running time: 9min 29s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             566.448 │
│ time_total_s                 566.448 │
│ training_iteration                 1 │
│ val_accuracy                 0.21342 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 17:01:47. Total running time: 9min 29s
[36m(train_cnn_ray_tune pid=3775655)[0m 
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2025-11-05 17:01:49,068	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_ESANN_acc_17_classes/ESANN_hyperparameters_tuning' in 0.0085s.
[36m(train_cnn_ray_tune pid=3775656)[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=3775656)[0m   _log_deprecation_warning(
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762358509.208507 3774011 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 trial_67ffe finished iteration 1 at 2025-11-05 17:01:49. Total running time: 9min 31s
╭──────────────────────────────────────╮
│ Trial trial_67ffe result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             568.217 │
│ time_total_s                 568.217 │
│ training_iteration                 1 │
│ val_accuracy                 0.22513 │
╰──────────────────────────────────────╯

Trial trial_67ffe completed after 1 iterations at 2025-11-05 17:01:49. Total running time: 9min 31s

Trial status: 20 TERMINATED
Current time: 2025-11-05 17:01:49. Total running time: 9min 31s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          3.37127e-05        119        1            315.866         0.188406 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00290294         146        1            360.527         0.287076 │
│ trial_67ffe    TERMINATED           3   adam            relu                                  128                128                  5          9.67942e-05         85        1            136.981         0.203891 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                  128                128                  3          0.000479242         97        1            440.549         0.313679 │
│ trial_67ffe    TERMINATED           4   adam            tanh                                   64                128                  3          5.0029e-05         137        1            566.448         0.213421 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          2.60076e-05        120        1            329.01          0.193568 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                   32                 32                  3          0.00128622         111        1            203.26          0.199921 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   64                 64                  3          0.000980258        118        1            330.596         0.216597 │
│ trial_67ffe    TERMINATED           3   adam            relu                                   32                 64                  3          0.000199406        132        1            526.895         0.325194 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                 32                  5          0.000685751        145        1            392.328         0.21481  │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   64                128                  3          7.12175e-05         81        1            483.274         0.271789 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 64                  5          0.000629084         74        1            430.901         0.248164 │
│ trial_67ffe    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.000408657         83        1            306.986         0.257495 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   32                 64                  3          0.00186374          89        1            429.864         0.244789 │
│ trial_67ffe    TERMINATED           2   rmsprop         tanh                                  128                 64                  5          0.000376048         62        1            309.492         0.251936 │
│ trial_67ffe    TERMINATED           3   adam            tanh                                   32                 64                  5          2.41462e-05         75        1            568.217         0.225134 │
│ trial_67ffe    TERMINATED           2   adam            relu                                  128                 32                  5          1.95135e-05         70        1            134.154         0.217788 │
│ trial_67ffe    TERMINATED           4   rmsprop         tanh                                   32                 32                  5          0.000767441         57        1            284.598         0.199126 │
│ trial_67ffe    TERMINATED           2   adam            tanh                                   32                 64                  3          0.000699197         84        1            542.971         0.303355 │
│ trial_67ffe    TERMINATED           3   rmsprop         relu                                  128                 32                  5          0.0014412           83        1            305.716         0.255112 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 3, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 32, 'numero_filtros': 64, 'tamanho_filtro': 3, 'tasa_aprendizaje': 0.00019940576714961078, 'epochs': 132}
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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762358511.455725 3874296 service.cc:152] XLA service 0x71b81c020260 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762358511.455755 3874296 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:01:51.497797: 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 00:00:1762358511.730953 3874296 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762358514.215724 3874296 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 419ms/step
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Saved model to disk.
[36m(train_cnn_ray_tune pid=3775656)[0m Epoch 75/75
[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
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[36m(train_cnn_ray_tune pid=3775656)[0m 
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3775656)[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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 62/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 822us/step
[1m130/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 780us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 20.45 [%]
Global F1 score (validation) = 10.96 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.03685325 0.05956395 0.03012297 ... 0.07362386 0.02986267 0.02596283]
 [0.03371603 0.05450141 0.03042388 ... 0.07840786 0.03093725 0.02673419]
 [0.04141614 0.06401706 0.0311609  ... 0.07498045 0.03085489 0.02613786]
 ...
 [0.09543457 0.06744723 0.10219017 ... 0.05494228 0.0986875  0.08561268]
 [0.09502319 0.06723939 0.10217838 ... 0.05527402 0.09899358 0.08547109]
 [0.09582397 0.06765399 0.10323702 ... 0.05464383 0.09975838 0.08615185]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 18.32 [%]
Global accuracy score (test) = 17.5 [%]
Global F1 score (train) = 9.97 [%]
Global F1 score (test) = 9.99 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.64      0.11      0.19       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.13      0.68      0.21       184
          DE PIE BARRIENDO       0.00      0.00      0.00       184
   DE PIE DOBLANDO TOALLAS       0.10      0.02      0.03       184
    DE PIE MOVIENDO LIBROS       0.09      0.13      0.11       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.18      0.78      0.29       184
INCREMENTAL CICLOERGOMETRO       0.29      0.68      0.41       184
           SENTADO LEYENDO       0.29      0.18      0.22       184
         SENTADO USANDO PC       0.00      0.00      0.00       184
      SENTADO VIENDO LA TV       0.00      0.00      0.00       184
   SUBIR Y BAJAR ESCALERAS       0.00      0.00      0.00       184
                    TROTAR       0.21      0.02      0.03       161

                  accuracy                           0.18      2737
                 macro avg       0.13      0.17      0.10      2737
              weighted avg       0.13      0.18      0.10      2737

2025-11-05 17:02:20.622100: 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 17:02:20.633720: 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 00:00:1762358540.647042 3875987 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762358540.651257 3875987 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762358540.661253 3875987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358540.661272 3875987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358540.661275 3875987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358540.661277 3875987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:02:20.664423: 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 00:00:1762358542.953303 3875987 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762358545.119104 3876121 service.cc:152] XLA service 0x72f1d0002c80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762358545.119134 3876121 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:02:25.163011: 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 00:00:1762358545.408854 3876121 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762358547.904941 3876121 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41:05[0m 4s/step - accuracy: 0.1250 - loss: 4.0159
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[1m177/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0592 - loss: 4.2692
[1m215/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0605 - loss: 4.2536
[1m247/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0613 - loss: 4.2407
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Epoch 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0998 - loss: 2.7374  
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1016 - loss: 2.7373
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[1m294/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1036 - loss: 2.7391
[1m333/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1038 - loss: 2.7392
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Epoch 7/132

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Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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Epoch 11/132

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[1m545/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1422 - loss: 2.5883
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.1423 - loss: 2.5880
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1423 - loss: 2.5880 - val_accuracy: 0.2063 - val_loss: 2.4242
Epoch 12/132

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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1859 - loss: 2.4378 - val_accuracy: 0.2650 - val_loss: 2.3231
Epoch 23/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1903 - loss: 2.4177  
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Epoch 24/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1921 - loss: 2.4370 - val_accuracy: 0.2744 - val_loss: 2.3101
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2500 - loss: 2.3045
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.3951  
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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.4207
[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2013 - loss: 2.4204
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2011 - loss: 2.4203 - val_accuracy: 0.2730 - val_loss: 2.3055
Epoch 26/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0938 - loss: 2.6284
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1799 - loss: 2.4362  
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[1m247/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.4239
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Epoch 27/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1951 - loss: 2.4127 - val_accuracy: 0.2754 - val_loss: 2.2941
Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2812 - loss: 2.3306
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2071 - loss: 2.4374  
[1m 75/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2054 - loss: 2.4345
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[1m223/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2031 - loss: 2.4235
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[1m296/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2011 - loss: 2.4224
[1m331/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2006 - loss: 2.4220
[1m368/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2002 - loss: 2.4214
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[1m479/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1996 - loss: 2.4193
[1m515/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1996 - loss: 2.4186
[1m551/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1996 - loss: 2.4180
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1996 - loss: 2.4174 - val_accuracy: 0.2787 - val_loss: 2.2987
Epoch 29/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2790
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Epoch 30/132

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Epoch 31/132

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[1m298/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1991 - loss: 2.3886
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Epoch 32/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2463 - loss: 2.3205  
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Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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Epoch 36/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.5312 - loss: 1.9479
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2410 - loss: 2.3236  
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[1m357/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2153 - loss: 2.3581
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[1m536/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2144 - loss: 2.3577
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2143 - loss: 2.3577 - val_accuracy: 0.2827 - val_loss: 2.2658
Epoch 37/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.1875 - loss: 2.5139
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2167 - loss: 2.3718  
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Epoch 38/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2202 - loss: 2.3631 - val_accuracy: 0.2875 - val_loss: 2.2524
Epoch 39/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.2412
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1966 - loss: 2.3781  
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[1m317/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2121 - loss: 2.3500
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2133 - loss: 2.3512
[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2134 - loss: 2.3515
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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2197 - loss: 2.3269 - val_accuracy: 0.3038 - val_loss: 2.2220
Epoch 50/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2344 - loss: 2.3021  
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[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2285 - loss: 2.3136
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Epoch 51/132

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Epoch 52/132

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Epoch 53/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.2519
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Epoch 54/132

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Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.1250 - loss: 2.5099
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1995 - loss: 2.3917  
[1m 67/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2093 - loss: 2.3643
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[1m317/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.3191
[1m353/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2233 - loss: 2.3179
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.3135
[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2246 - loss: 2.3129
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2246 - loss: 2.3128 - val_accuracy: 0.3161 - val_loss: 2.2056
Epoch 56/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 2.2281
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2420 - loss: 2.2732  
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.2881
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Epoch 57/132

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Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 2.1165
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2363 - loss: 2.2815  
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Epoch 59/132

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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.3146
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Epoch 68/132

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Epoch 69/132

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Epoch 70/132

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Epoch 71/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2405 - loss: 2.2653 - val_accuracy: 0.3274 - val_loss: 2.1958
Epoch 72/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2500 - loss: 2.0828
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2293 - loss: 2.2433  
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[1m294/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2370 - loss: 2.2631
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[1m366/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2382 - loss: 2.2631
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2399 - loss: 2.2641 - val_accuracy: 0.3190 - val_loss: 2.1874
Epoch 73/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1887
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Epoch 74/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2421 - loss: 2.2630 - val_accuracy: 0.3302 - val_loss: 2.1890
Epoch 75/132

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Epoch 76/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2423 - loss: 2.2643 - val_accuracy: 0.3302 - val_loss: 2.1767
Epoch 77/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.2614  
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[1m534/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2403 - loss: 2.2712
[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2401 - loss: 2.2711
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2401 - loss: 2.2710 - val_accuracy: 0.3286 - val_loss: 2.1805
Epoch 78/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 23ms/step - accuracy: 0.1562 - loss: 2.2331
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2525 - loss: 2.2339  
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Epoch 79/132

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Epoch 80/132

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Epoch 81/132

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Epoch 82/132

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Epoch 83/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2485 - loss: 2.2290  
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Epoch 84/132

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Saved model to disk.

Accuracy capturado en la ejecución 1: 17.5 [%]
F1-score capturado en la ejecución 1: 9.99 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 810us/step
[1m123/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 828us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 33.51 [%]
Global F1 score (validation) = 29.33 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00619236 0.00985736 0.005149   ... 0.08720931 0.00672712 0.00226152]
 [0.00346966 0.00495073 0.00307645 ... 0.09764941 0.0038297  0.00150142]
 [0.00205064 0.00321489 0.00168725 ... 0.06518842 0.00232378 0.00066653]
 ...
 [0.16160174 0.0576969  0.14749904 ... 0.01698744 0.17498834 0.0775783 ]
 [0.09722384 0.08642142 0.09774254 ... 0.04484952 0.09322681 0.05363866]
 [0.18997449 0.0369051  0.16793567 ... 0.00405386 0.21080773 0.11816749]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 30.94 [%]
Global accuracy score (test) = 29.63 [%]
Global F1 score (train) = 27.89 [%]
Global F1 score (test) = 26.04 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.37      0.30       184
       CAMINAR USUAL SPEED       0.02      0.01      0.01       184
            CAMINAR ZIGZAG       0.23      0.46      0.30       184
          DE PIE BARRIENDO       0.13      0.03      0.05       184
   DE PIE DOBLANDO TOALLAS       0.22      0.40      0.29       184
    DE PIE MOVIENDO LIBROS       0.28      0.24      0.26       184
          DE PIE USANDO PC       0.06      0.01      0.02       184
        FASE REPOSO CON K5       0.37      0.76      0.49       184
INCREMENTAL CICLOERGOMETRO       0.43      0.53      0.47       184
           SENTADO LEYENDO       0.42      0.39      0.40       184
         SENTADO USANDO PC       0.08      0.04      0.05       184
      SENTADO VIENDO LA TV       0.34      0.33      0.33       184
   SUBIR Y BAJAR ESCALERAS       0.25      0.37      0.30       184
                    TROTAR       0.75      0.56      0.64       161

                  accuracy                           0.30      2737
                 macro avg       0.25      0.30      0.26      2737
              weighted avg       0.25      0.30      0.26      2737

2025-11-05 17:04:11.953887: 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 17:04:11.965335: 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 00:00:1762358651.978563 3885363 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762358651.982737 3885363 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762358651.992761 3885363 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358651.992781 3885363 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358651.992783 3885363 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358651.992785 3885363 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:04:11.995961: 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 00:00:1762358654.269494 3885363 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762358656.454965 3885503 service.cc:152] XLA service 0x7964d0006970 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762358656.454996 3885503 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:04:16.514531: 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 00:00:1762358656.755114 3885503 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762358659.251450 3885503 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 791us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 2: 29.63 [%]
F1-score capturado en la ejecución 2: 26.04 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 740us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 72/158[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 719us/step
[1m144/158[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 708us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 18.68 [%]
Global F1 score (validation) = 8.26 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.04088777 0.06702637 0.03641415 ... 0.07071176 0.03396447 0.03734728]
 [0.04094391 0.06578073 0.03583914 ... 0.07248437 0.03443761 0.03591669]
 [0.04177544 0.06776772 0.03732587 ... 0.07260095 0.03439305 0.03734431]
 ...
 [0.11466615 0.05633817 0.11666961 ... 0.05076772 0.11152472 0.10139272]
 [0.11302005 0.05677708 0.11565737 ... 0.05154527 0.11116195 0.10028736]
 [0.11500443 0.05513389 0.11706544 ... 0.05007324 0.1130609  0.10204098]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 16.29 [%]
Global accuracy score (test) = 16.62 [%]
Global F1 score (train) = 7.27 [%]
Global F1 score (test) = 7.88 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.14      0.68      0.23       184
          DE PIE BARRIENDO       0.22      0.01      0.02       184
   DE PIE DOBLANDO TOALLAS       0.13      0.02      0.04       184
    DE PIE MOVIENDO LIBROS       0.00      0.00      0.00       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.14      0.88      0.24       184
INCREMENTAL CICLOERGOMETRO       0.25      0.70      0.37       184
           SENTADO LEYENDO       0.39      0.16      0.22       184
         SENTADO USANDO PC       0.00      0.00      0.00       184
      SENTADO VIENDO LA TV       0.00      0.00      0.00       184
   SUBIR Y BAJAR ESCALERAS       0.00      0.00      0.00       184
                    TROTAR       0.71      0.03      0.06       161

                  accuracy                           0.17      2737
                 macro avg       0.13      0.17      0.08      2737
              weighted avg       0.13      0.17      0.08      2737

2025-11-05 17:04:43.486444: 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 17:04:43.497705: 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 00:00:1762358683.510653 3887190 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762358683.514749 3887190 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762358683.524475 3887190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358683.524498 3887190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358683.524500 3887190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358683.524502 3887190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:04:43.527613: 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 00:00:1762358685.780493 3887190 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762358687.950193 3887299 service.cc:152] XLA service 0x7ce780017030 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762358687.950245 3887299 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:04:48.005547: 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 00:00:1762358688.255202 3887299 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762358690.734569 3887299 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0817 - loss: 2.7964  
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Epoch 7/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1109 - loss: 2.7033 - val_accuracy: 0.2096 - val_loss: 2.4782
Epoch 8/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1211 - loss: 2.6671  
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1145 - loss: 2.6712
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[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1168 - loss: 2.6725
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[1m534/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1179 - loss: 2.6706
[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1180 - loss: 2.6700
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1181 - loss: 2.6698 - val_accuracy: 0.2093 - val_loss: 2.4670
Epoch 9/132

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Epoch 10/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1349 - loss: 2.6076 - val_accuracy: 0.2132 - val_loss: 2.4437
Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1612 - loss: 2.5502 - val_accuracy: 0.2182 - val_loss: 2.3946
Epoch 14/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1546 - loss: 2.5230  
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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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Epoch 23/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1901 - loss: 2.4415 - val_accuracy: 0.2486 - val_loss: 2.3338
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 2.2520
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1948 - loss: 2.3819  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1957 - loss: 2.3982
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[1m426/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1943 - loss: 2.4207
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[1m536/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.4227
[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.4232
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1939 - loss: 2.4232 - val_accuracy: 0.2428 - val_loss: 2.3284
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4101
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1843 - loss: 2.4456  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1879 - loss: 2.4386
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Epoch 26/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1985 - loss: 2.4068 - val_accuracy: 0.2585 - val_loss: 2.3170
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.6388
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.4161  
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1956 - loss: 2.4134
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[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2004 - loss: 2.4041
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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2007 - loss: 2.4039
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2007 - loss: 2.4043 - val_accuracy: 0.2503 - val_loss: 2.3108
Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4618
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Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2024 - loss: 2.4028 - val_accuracy: 0.2621 - val_loss: 2.2973
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4677
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1859 - loss: 2.4123  
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Epoch 31/132

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Epoch 32/132

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[1m527/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2077 - loss: 2.3790
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2078 - loss: 2.3795 - val_accuracy: 0.2865 - val_loss: 2.2846
Epoch 33/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2230 - loss: 2.3606  
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Epoch 34/132

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Epoch 35/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2037 - loss: 2.3791  
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Epoch 36/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2178 - loss: 2.3519  
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Epoch 37/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3533 - val_accuracy: 0.2912 - val_loss: 2.2616
Epoch 38/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.1858
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2214 - loss: 2.3246  
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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2133 - loss: 2.3538
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2131 - loss: 2.3541
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Epoch 39/132

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Epoch 40/132

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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2184 - loss: 2.3544
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Epoch 41/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1250 - loss: 2.4188
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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2273 - loss: 2.3284 - val_accuracy: 0.3083 - val_loss: 2.2203
Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1949
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2419 - loss: 2.2673  
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Epoch 53/132

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[1m530/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2335 - loss: 2.3048
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2332 - loss: 2.3056 - val_accuracy: 0.3151 - val_loss: 2.2111
Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.3342
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2345 - loss: 2.3379  
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[1m532/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2263 - loss: 2.3302
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2266 - loss: 2.3288 - val_accuracy: 0.3079 - val_loss: 2.2123
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3170
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2042 - loss: 2.2985  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2128 - loss: 2.2793
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Epoch 56/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2276 - loss: 2.3125 - val_accuracy: 0.3133 - val_loss: 2.2092
Epoch 57/132

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[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2358 - loss: 2.2360  
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[1m337/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2325 - loss: 2.2827
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Epoch 58/132

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Epoch 59/132

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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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Epoch 68/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2356 - loss: 2.2875  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2345 - loss: 2.2808
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Epoch 69/132

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Epoch 70/132

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Epoch 71/132

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Epoch 72/132

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Epoch 73/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2384 - loss: 2.2717 - val_accuracy: 0.3224 - val_loss: 2.1815
Epoch 74/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2488 - loss: 2.2523  
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Epoch 75/132

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Epoch 76/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2495 - loss: 2.2904  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2371 - loss: 2.2767 - val_accuracy: 0.3272 - val_loss: 2.1796
Epoch 77/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 2.5761
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2408 - loss: 2.3601  
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2425 - loss: 2.3121
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[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 771us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 3: 16.62 [%]
F1-score capturado en la ejecución 3: 7.88 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 738us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 65/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 787us/step
[1m131/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 773us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 32.66 [%]
Global F1 score (validation) = 27.32 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00613725 0.00874868 0.00424586 ... 0.07342824 0.00686676 0.00190358]
 [0.00450792 0.00590222 0.0035861  ... 0.09102831 0.00509058 0.00174543]
 [0.00295303 0.00351569 0.00212686 ... 0.05874359 0.00351771 0.00094284]
 ...
 [0.1564283  0.0572653  0.14761314 ... 0.01686674 0.1665045  0.09230787]
 [0.15651424 0.06242063 0.15144287 ... 0.01572139 0.16469292 0.09337473]
 [0.16991302 0.04773002 0.16441423 ... 0.00777504 0.18849611 0.11864436]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 29.64 [%]
Global accuracy score (test) = 28.79 [%]
Global F1 score (train) = 25.43 [%]
Global F1 score (test) = 24.77 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.32      0.31       184
       CAMINAR USUAL SPEED       0.05      0.03      0.04       184
            CAMINAR ZIGZAG       0.20      0.64      0.30       184
          DE PIE BARRIENDO       0.05      0.01      0.02       184
   DE PIE DOBLANDO TOALLAS       0.24      0.44      0.31       184
    DE PIE MOVIENDO LIBROS       0.26      0.28      0.27       184
          DE PIE USANDO PC       0.39      0.05      0.09       184
        FASE REPOSO CON K5       0.36      0.76      0.49       184
INCREMENTAL CICLOERGOMETRO       0.41      0.58      0.48       184
           SENTADO LEYENDO       0.40      0.39      0.39       184
         SENTADO USANDO PC       0.07      0.03      0.04       184
      SENTADO VIENDO LA TV       0.25      0.21      0.22       184
   SUBIR Y BAJAR ESCALERAS       0.21      0.08      0.12       184
                    TROTAR       0.80      0.55      0.65       161

                  accuracy                           0.29      2737
                 macro avg       0.27      0.29      0.25      2737
              weighted avg       0.26      0.29      0.24      2737

2025-11-05 17:06:27.789947: 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 17:06:27.801058: 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 00:00:1762358787.813957 3895858 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762358787.817914 3895858 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762358787.827795 3895858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358787.827813 3895858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358787.827815 3895858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358787.827817 3895858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:06:27.830957: 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 00:00:1762358790.110008 3895858 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762358792.220142 3895997 service.cc:152] XLA service 0x70d99c00e7a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762358792.220205 3895997 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:06:32.270788: 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 00:00:1762358792.518213 3895997 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762358795.020522 3895997 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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[1m540/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1535 - loss: 2.5694
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1534 - loss: 2.5694 - val_accuracy: 0.2267 - val_loss: 2.4165
Epoch 14/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.1875 - loss: 2.4585
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1793 - loss: 2.5369  
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Epoch 15/132

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Epoch 16/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1686 - loss: 2.5131 - val_accuracy: 0.2275 - val_loss: 2.3717
Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1747 - loss: 2.4707  
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Epoch 21/132

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Epoch 22/132

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Epoch 23/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1979 - loss: 2.4404  
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Epoch 24/132

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Epoch 25/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1966 - loss: 2.4311  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1931 - loss: 2.4312 - val_accuracy: 0.2629 - val_loss: 2.3066
Epoch 26/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1990 - loss: 2.4095 - val_accuracy: 0.2742 - val_loss: 2.2930
Epoch 27/132

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[1m509/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.3988
[1m546/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2036 - loss: 2.3992
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2035 - loss: 2.3996 - val_accuracy: 0.2678 - val_loss: 2.2833
Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1521
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2240 - loss: 2.3279  
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Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2054 - loss: 2.3904 - val_accuracy: 0.2724 - val_loss: 2.2744
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4587
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1984 - loss: 2.3951  
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[1m508/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1997 - loss: 2.4002
[1m544/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2000 - loss: 2.3997
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2004 - loss: 2.3992 - val_accuracy: 0.2851 - val_loss: 2.2572
Epoch 31/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5467
[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.3851  
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Epoch 32/132

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Epoch 33/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1964 - loss: 2.3722  
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Epoch 34/132

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Epoch 35/132

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Epoch 36/132

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Epoch 37/132

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Epoch 38/132

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Epoch 39/132

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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.5827
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.4086  
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[1m289/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2145 - loss: 2.3508
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Epoch 45/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4081
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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2260 - loss: 2.3158
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2255 - loss: 2.3170 - val_accuracy: 0.3204 - val_loss: 2.1982
Epoch 50/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2352 - loss: 2.3030  
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Epoch 51/132

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Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.4515
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 2.3266  
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[1m557/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2286 - loss: 2.3192
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2285 - loss: 2.3190 - val_accuracy: 0.3157 - val_loss: 2.1919
Epoch 53/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.3612
[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2290 - loss: 2.3005  
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Epoch 54/132

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Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.4022
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2058 - loss: 2.3702  
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[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2210 - loss: 2.3182
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[1m550/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2233 - loss: 2.3134
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2234 - loss: 2.3130 - val_accuracy: 0.3163 - val_loss: 2.1867
Epoch 56/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 2.3287
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2637 - loss: 2.3089  
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Epoch 57/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2282 - loss: 2.2946 - val_accuracy: 0.3238 - val_loss: 2.1819
Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1194
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2145 - loss: 2.3138  
[1m 67/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2142 - loss: 2.3160
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[1m180/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2179 - loss: 2.3073
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[1m294/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.3021
[1m326/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.3015
[1m366/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2238 - loss: 2.3010
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[1m549/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2256 - loss: 2.3007
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Epoch 59/132

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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2320 - loss: 2.2931 - val_accuracy: 0.3282 - val_loss: 2.1660
Epoch 64/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3444
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2270 - loss: 2.2774  
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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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Epoch 68/132

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Epoch 69/132

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Epoch 70/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2596 - loss: 2.2614  
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Epoch 71/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2414 - loss: 2.2657 - val_accuracy: 0.3516 - val_loss: 2.1521
Epoch 72/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2506 - loss: 2.2351  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2508 - loss: 2.2403
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[1m303/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2421 - loss: 2.2593
[1m342/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2417 - loss: 2.2596
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[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2398 - loss: 2.2608
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Epoch 73/132

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Epoch 74/132

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[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 754us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 4: 28.79 [%]
F1-score capturado en la ejecución 4: 24.77 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 64/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 804us/step
[1m125/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 817us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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Global accuracy score (validation) = 34.98 [%]
Global F1 score (validation) = 30.68 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00367008 0.00513451 0.0030599  ... 0.0836931  0.00401652 0.00125805]
 [0.00436517 0.00534848 0.00398466 ... 0.09935461 0.00482929 0.00186124]
 [0.00186673 0.0027618  0.00158204 ... 0.07914148 0.00216441 0.00055609]
 ...
 [0.15135539 0.06553949 0.14363521 ... 0.02073624 0.16302676 0.08274443]
 [0.16693121 0.06248596 0.16102624 ... 0.01137081 0.1829939  0.09590667]
 [0.13914539 0.06494536 0.13146308 ... 0.02848595 0.14813446 0.07707129]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 31.21 [%]
Global accuracy score (test) = 29.41 [%]
Global F1 score (train) = 27.91 [%]
Global F1 score (test) = 26.47 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.38      0.30       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.18      0.30      0.23       184
          DE PIE BARRIENDO       0.14      0.05      0.08       184
   DE PIE DOBLANDO TOALLAS       0.25      0.36      0.30       184
    DE PIE MOVIENDO LIBROS       0.33      0.30      0.31       184
          DE PIE USANDO PC       0.57      0.07      0.12       184
        FASE REPOSO CON K5       0.34      0.76      0.47       184
INCREMENTAL CICLOERGOMETRO       0.41      0.51      0.45       184
           SENTADO LEYENDO       0.37      0.41      0.39       184
         SENTADO USANDO PC       0.13      0.07      0.09       184
      SENTADO VIENDO LA TV       0.31      0.26      0.28       184
   SUBIR Y BAJAR ESCALERAS       0.24      0.44      0.31       184
                    TROTAR       0.81      0.55      0.65       161

                  accuracy                           0.29      2737
                 macro avg       0.29      0.30      0.26      2737
              weighted avg       0.28      0.29      0.26      2737

2025-11-05 17:08:08.205870: 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 17:08:08.217062: 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 00:00:1762358888.230178 3904241 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762358888.234100 3904241 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762358888.243913 3904241 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358888.243930 3904241 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358888.243932 3904241 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358888.243933 3904241 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:08:08.246901: 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 00:00:1762358890.500147 3904241 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762358892.631431 3904369 service.cc:152] XLA service 0x78ff90006280 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762358892.631469 3904369 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:08:12.680610: 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 00:00:1762358892.920389 3904369 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762358895.388430 3904369 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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Epoch 9/132

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[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1236 - loss: 2.6480
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Epoch 10/132

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Epoch 11/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1364 - loss: 2.6029 - val_accuracy: 0.2007 - val_loss: 2.4217
Epoch 12/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1245 - loss: 2.5920  
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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m529/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1743 - loss: 2.4829
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1746 - loss: 2.4822 - val_accuracy: 0.2243 - val_loss: 2.3552
Epoch 21/132

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Epoch 22/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1851 - loss: 2.4616 - val_accuracy: 0.2442 - val_loss: 2.3410
Epoch 23/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1824 - loss: 2.4748  
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[1m535/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1869 - loss: 2.4547
[1m571/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1872 - loss: 2.4545
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1873 - loss: 2.4545 - val_accuracy: 0.2583 - val_loss: 2.3330
Epoch 24/132

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Epoch 25/132

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Epoch 26/132

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Epoch 27/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1934 - loss: 2.4243 - val_accuracy: 0.2738 - val_loss: 2.3095
Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.5621
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[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1996 - loss: 2.4199
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1994 - loss: 2.4201
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1994 - loss: 2.4202 - val_accuracy: 0.2662 - val_loss: 2.3065
Epoch 29/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.6495
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1834 - loss: 2.4274  
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Epoch 30/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1921 - loss: 2.4223 - val_accuracy: 0.2720 - val_loss: 2.2908
Epoch 31/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.4053  
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[1m331/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2035 - loss: 2.4104
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Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.4165  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2044 - loss: 2.4004 - val_accuracy: 0.2821 - val_loss: 2.2781
Epoch 36/132

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[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1913 - loss: 2.3885
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[1m528/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2027 - loss: 2.3854
[1m562/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2029 - loss: 2.3853
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2030 - loss: 2.3853 - val_accuracy: 0.2893 - val_loss: 2.2715
Epoch 37/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.2730
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2041 - loss: 2.3544  
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Epoch 38/132

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Epoch 39/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2134
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2173 - loss: 2.3221  
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[1m330/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2126 - loss: 2.3661
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2117 - loss: 2.3709
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2117 - loss: 2.3709 - val_accuracy: 0.2871 - val_loss: 2.2700
Epoch 40/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3750 - loss: 2.1161
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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2180 - loss: 2.3731
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2181 - loss: 2.3720 - val_accuracy: 0.3043 - val_loss: 2.2418
Epoch 45/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.2248
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2155 - loss: 2.3198  
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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2165 - loss: 2.3439 - val_accuracy: 0.2950 - val_loss: 2.2270
Epoch 50/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2465 - loss: 2.3174  
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[1m219/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2304 - loss: 2.3335
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[1m296/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2297 - loss: 2.3333
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[1m371/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2290 - loss: 2.3331
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[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2274 - loss: 2.3332
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2271 - loss: 2.3333 - val_accuracy: 0.3049 - val_loss: 2.2236
Epoch 51/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.4454
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2083 - loss: 2.3671  
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Epoch 52/132

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Epoch 53/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2199 - loss: 2.3357 - val_accuracy: 0.3043 - val_loss: 2.2171
Epoch 54/132

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Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0938 - loss: 2.4988
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.3484  
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[1m217/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2345 - loss: 2.3192
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[1m291/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.3209
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[1m508/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2298 - loss: 2.3226
[1m544/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.3231
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2292 - loss: 2.3235 - val_accuracy: 0.3032 - val_loss: 2.2165
Epoch 56/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.1875 - loss: 2.4321
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2347 - loss: 2.2994  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2358 - loss: 2.3006
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Epoch 57/132

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Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.2489
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2018 - loss: 2.3307  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2235 - loss: 2.3219 - val_accuracy: 0.3135 - val_loss: 2.2072
Epoch 59/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0938 - loss: 2.4831
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2046 - loss: 2.3825  
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Epoch 60/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2316 - loss: 2.3116 - val_accuracy: 0.3071 - val_loss: 2.2083
Epoch 61/132

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Epoch 62/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.3059  
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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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Epoch 68/132

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Epoch 69/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2320 - loss: 2.2896 - val_accuracy: 0.3147 - val_loss: 2.1907
Epoch 70/132

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Epoch 71/132

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Epoch 72/132

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[1m 40/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2209 - loss: 2.2926  
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[1m380/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2306 - loss: 2.2846
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[1m530/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2325 - loss: 2.2829
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2328 - loss: 2.2827
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2329 - loss: 2.2827 - val_accuracy: 0.3317 - val_loss: 2.1813
Epoch 73/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.4393
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2296 - loss: 2.2999  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.3013
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Epoch 74/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2374 - loss: 2.2692 - val_accuracy: 0.3317 - val_loss: 2.1774
Epoch 75/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4746
[1m 29/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2226 - loss: 2.2904  
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[1m283/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2353 - loss: 2.2732
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2336 - loss: 2.2758
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2338 - loss: 2.2758 - val_accuracy: 0.3290 - val_loss: 2.1788
Epoch 76/132

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Epoch 77/132

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Epoch 78/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2585 - loss: 2.2393  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2533 - loss: 2.2527
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[1m283/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2484 - loss: 2.2634
[1m318/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2477 - loss: 2.2640
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Epoch 79/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2428 - loss: 2.2570 - val_accuracy: 0.3389 - val_loss: 2.1803
Epoch 80/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2317 - loss: 2.2645
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2317 - loss: 2.2645 - val_accuracy: 0.3427 - val_loss: 2.1759
Epoch 81/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2507 - loss: 2.2318  
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[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 807us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 5: 29.41 [%]
F1-score capturado en la ejecución 5: 26.47 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 752us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 70/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 734us/step
[1m142/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 717us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 33.57 [%]
Global F1 score (validation) = 29.85 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00931315 0.01442357 0.00692558 ... 0.08124416 0.01068793 0.00385237]
 [0.00679386 0.01043849 0.00577253 ... 0.10578448 0.00840145 0.00310445]
 [0.00295687 0.00489192 0.00220886 ... 0.07844105 0.00342449 0.00121604]
 ...
 [0.16908963 0.05605843 0.14539078 ... 0.01475596 0.18014951 0.08316367]
 [0.07744338 0.06630542 0.06962433 ... 0.07890001 0.07745057 0.03369609]
 [0.17125711 0.04914556 0.14479531 ... 0.0134578  0.1886188  0.08749953]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 31.3 [%]
Global accuracy score (test) = 29.81 [%]
Global F1 score (train) = 28.3 [%]
Global F1 score (test) = 26.21 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.36      0.32       184
       CAMINAR USUAL SPEED       0.05      0.03      0.04       184
            CAMINAR ZIGZAG       0.22      0.63      0.33       184
          DE PIE BARRIENDO       0.20      0.06      0.09       184
   DE PIE DOBLANDO TOALLAS       0.25      0.43      0.31       184
    DE PIE MOVIENDO LIBROS       0.30      0.28      0.29       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.36      0.76      0.49       184
INCREMENTAL CICLOERGOMETRO       0.41      0.50      0.45       184
           SENTADO LEYENDO       0.47      0.39      0.42       184
         SENTADO USANDO PC       0.08      0.02      0.03       184
      SENTADO VIENDO LA TV       0.29      0.34      0.31       184
   SUBIR Y BAJAR ESCALERAS       0.25      0.17      0.20       184
                    TROTAR       0.83      0.54      0.65       161

                  accuracy                           0.30      2737
                 macro avg       0.26      0.30      0.26      2737
              weighted avg       0.26      0.30      0.26      2737

2025-11-05 17:09:56.591980: 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 17:09:56.603377: 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 00:00:1762358996.616538 3913352 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762358996.620849 3913352 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762358996.631253 3913352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358996.631273 3913352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358996.631275 3913352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762358996.631277 3913352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:09:56.634462: 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 00:00:1762358998.881436 3913352 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359001.089351 3913460 service.cc:152] XLA service 0x74f2ac011910 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359001.089382 3913460 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:10:01.135991: 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 00:00:1762359001.378655 3913460 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359003.851640 3913460 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1166 - loss: 2.7528  
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Epoch 7/132

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Epoch 8/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1091 - loss: 2.6976  
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Epoch 9/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1225 - loss: 2.6727  
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Epoch 10/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1371 - loss: 2.6113 - val_accuracy: 0.2108 - val_loss: 2.4401
Epoch 11/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1570 - loss: 2.5759  
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[1m216/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1468 - loss: 2.5888
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1431 - loss: 2.5945 - val_accuracy: 0.2053 - val_loss: 2.4290
Epoch 12/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4846
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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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Epoch 23/132

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Epoch 24/132

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[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1917 - loss: 2.4329
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1918 - loss: 2.4328 - val_accuracy: 0.2541 - val_loss: 2.3135
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4232
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1842 - loss: 2.4260  
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Epoch 26/132

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Epoch 27/132

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Epoch 28/132

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Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1997 - loss: 2.4009 - val_accuracy: 0.2700 - val_loss: 2.2926
Epoch 30/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2237 - loss: 2.3252  
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[1m319/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2092 - loss: 2.3846
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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2069 - loss: 2.3889
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2066 - loss: 2.3893
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Epoch 31/132

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Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1970 - loss: 2.3979  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1960 - loss: 2.4008
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[1m572/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.3869
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2014 - loss: 2.3869 - val_accuracy: 0.2718 - val_loss: 2.2909
Epoch 35/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2544
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2062 - loss: 2.3879  
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[1m330/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2064 - loss: 2.3880
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[1m439/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2072 - loss: 2.3862
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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.3853
[1m548/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2081 - loss: 2.3850
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2082 - loss: 2.3848 - val_accuracy: 0.2875 - val_loss: 2.2747
Epoch 36/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2777
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1826 - loss: 2.3850  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.3881
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Epoch 37/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2157 - loss: 2.3647 - val_accuracy: 0.2861 - val_loss: 2.2669
Epoch 38/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2041 - loss: 2.3750  
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Epoch 39/132

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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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[1m540/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2172 - loss: 2.3548
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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2308 - loss: 2.3293  
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[1m314/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2244 - loss: 2.3349
[1m352/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2241 - loss: 2.3352
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2223 - loss: 2.3357 - val_accuracy: 0.3153 - val_loss: 2.2369
Epoch 50/132

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Epoch 51/132

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Epoch 52/132

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Epoch 53/132

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Epoch 54/132

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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.3249
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.3248
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2227 - loss: 2.3248 - val_accuracy: 0.3159 - val_loss: 2.2280
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0938 - loss: 2.4888
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2202 - loss: 2.3066  
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Epoch 56/132

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Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.3125 - loss: 2.2940
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[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2306 - loss: 2.3110
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2283 - loss: 2.3137
[1m573/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2281 - loss: 2.3138
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2281 - loss: 2.3139 - val_accuracy: 0.3034 - val_loss: 2.2387
Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2733
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[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2034 - loss: 2.3312
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Epoch 59/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2315 - loss: 2.3104 - val_accuracy: 0.3159 - val_loss: 2.2211
Epoch 60/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2077 - loss: 2.3324  
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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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Epoch 68/132

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Epoch 69/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2219 - loss: 2.3234  
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Epoch 70/132

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Epoch 71/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2477 - loss: 2.2911  
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[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2372 - loss: 2.2868
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2363 - loss: 2.2869 - val_accuracy: 0.3230 - val_loss: 2.1953
Epoch 72/132

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[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2412 - loss: 2.2934
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Epoch 73/132

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Epoch 74/132

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Epoch 75/132

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Epoch 76/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2365 - loss: 2.2703 - val_accuracy: 0.3339 - val_loss: 2.1921
Epoch 77/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 2.1557
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2408 - loss: 2.2433  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2350 - loss: 2.2562
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[1m223/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2350 - loss: 2.2595
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[1m293/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.2604
[1m328/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2363 - loss: 2.2607
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Epoch 78/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2357 - loss: 2.2769 - val_accuracy: 0.3313 - val_loss: 2.1910
Epoch 79/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2356 - loss: 2.2742 - val_accuracy: 0.3226 - val_loss: 2.1882
Epoch 80/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.4471
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2600 - loss: 2.2776  
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Epoch 81/132

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Epoch 82/132

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Epoch 83/132

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Epoch 84/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2478 - loss: 2.2557 - val_accuracy: 0.3353 - val_loss: 2.1883
Epoch 85/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2393 - loss: 2.2730  
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Epoch 86/132

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Epoch 87/132

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Epoch 88/132

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Epoch 89/132

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Epoch 90/132

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Epoch 91/132

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Epoch 92/132

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Epoch 93/132

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Epoch 94/132

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[1m441/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.2444
[1m473/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2487 - loss: 2.2442
[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2488 - loss: 2.2439
[1m546/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2489 - loss: 2.2436
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2489 - loss: 2.2433 - val_accuracy: 0.3290 - val_loss: 2.1804
Epoch 95/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2220 - loss: 2.3068  
[1m 75/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2381 - loss: 2.2731
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[1m149/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.2550
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[1m222/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.2510
[1m259/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.2494
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[1m550/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2448 - loss: 2.2439
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2448 - loss: 2.2437 - val_accuracy: 0.3359 - val_loss: 2.1646

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[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 759us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 6: 29.81 [%]
F1-score capturado en la ejecución 6: 26.21 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m134/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 759us/step
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[1m272/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 744us/step
[1m339/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 746us/step
[1m396/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 766us/step
[1m460/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 769us/step
[1m518/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 779us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 774us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 64/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 798us/step
[1m132/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 769us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 33.59 [%]
Global F1 score (validation) = 28.78 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00580991 0.00707164 0.00471651 ... 0.08179314 0.00636893 0.00204084]
 [0.00378479 0.00427204 0.00341019 ... 0.09565867 0.0041699  0.00166981]
 [0.00193761 0.0024455  0.0015761  ... 0.07658812 0.00227728 0.00061328]
 ...
 [0.14905633 0.05321275 0.1391458  ... 0.02054205 0.17006764 0.07868343]
 [0.06310257 0.05850837 0.05997195 ... 0.09324972 0.05788608 0.03188593]
 [0.14930886 0.0640831  0.14003833 ... 0.01984658 0.16460541 0.07735057]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 30.79 [%]
Global accuracy score (test) = 29.01 [%]
Global F1 score (train) = 27.09 [%]
Global F1 score (test) = 25.86 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.38      0.31       184
       CAMINAR USUAL SPEED       0.05      0.02      0.02       184
            CAMINAR ZIGZAG       0.20      0.33      0.25       184
          DE PIE BARRIENDO       0.35      0.07      0.12       184
   DE PIE DOBLANDO TOALLAS       0.27      0.46      0.34       184
    DE PIE MOVIENDO LIBROS       0.29      0.28      0.29       184
          DE PIE USANDO PC       0.30      0.04      0.07       184
        FASE REPOSO CON K5       0.35      0.76      0.48       184
INCREMENTAL CICLOERGOMETRO       0.40      0.52      0.45       184
           SENTADO LEYENDO       0.41      0.39      0.40       184
         SENTADO USANDO PC       0.14      0.07      0.09       184
      SENTADO VIENDO LA TV       0.24      0.22      0.23       184
   SUBIR Y BAJAR ESCALERAS       0.18      0.35      0.24       184
                    TROTAR       0.72      0.51      0.60       161

                  accuracy                           0.29      2737
                 macro avg       0.28      0.29      0.26      2737
              weighted avg       0.27      0.29      0.26      2737

2025-11-05 17:11:59.473558: 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 17:11:59.484915: 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 00:00:1762359119.498374 3923828 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359119.502575 3923828 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359119.512554 3923828 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359119.512574 3923828 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359119.512576 3923828 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359119.512577 3923828 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:11:59.515752: 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 00:00:1762359121.764820 3923828 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359123.925963 3923961 service.cc:152] XLA service 0x7fdecc011830 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359123.925999 3923961 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:12:03.969533: 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 00:00:1762359124.204304 3923961 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359126.662400 3923961 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1420 - loss: 2.6141  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1359 - loss: 2.6212 - val_accuracy: 0.2015 - val_loss: 2.4461
Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1849 - loss: 2.4622 - val_accuracy: 0.2243 - val_loss: 2.3486
Epoch 22/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2029 - loss: 2.4450  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2031 - loss: 2.4430
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[1m326/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1964 - loss: 2.4496
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[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1930 - loss: 2.4519
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1928 - loss: 2.4520 - val_accuracy: 0.2357 - val_loss: 2.3409
Epoch 23/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.4073
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2130 - loss: 2.4101  
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[1m315/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1901 - loss: 2.4339
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[1m456/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1890 - loss: 2.4366
[1m490/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1889 - loss: 2.4370
[1m524/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1888 - loss: 2.4373
[1m557/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1887 - loss: 2.4376
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1887 - loss: 2.4379 - val_accuracy: 0.2432 - val_loss: 2.3315
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.6000
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1900 - loss: 2.4847  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1922 - loss: 2.4594
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Epoch 25/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1911 - loss: 2.4317 - val_accuracy: 0.2559 - val_loss: 2.3150
Epoch 26/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.4311
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1835 - loss: 2.4395  
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Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0625 - loss: 2.5989
[1m 29/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1687 - loss: 2.4887  
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Epoch 28/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1934 - loss: 2.4146 - val_accuracy: 0.2635 - val_loss: 2.3029
Epoch 29/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.3241
[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1897 - loss: 2.4241  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1966 - loss: 2.4331
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[1m559/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2004 - loss: 2.4159
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2004 - loss: 2.4155 - val_accuracy: 0.2642 - val_loss: 2.3004
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0625 - loss: 2.5129
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Epoch 31/132

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[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2009 - loss: 2.3919
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2008 - loss: 2.3921 - val_accuracy: 0.2660 - val_loss: 2.2968
Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1967 - loss: 2.3824  
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Epoch 35/132

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Epoch 36/132

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[1m530/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2091 - loss: 2.3768
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2091 - loss: 2.3765 - val_accuracy: 0.2859 - val_loss: 2.2633
Epoch 37/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0625 - loss: 2.5363
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1989 - loss: 2.4371  
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Epoch 38/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2188 - loss: 2.3712
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2284 - loss: 2.3496  
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Epoch 39/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2202 - loss: 2.3471 - val_accuracy: 0.2998 - val_loss: 2.2538
Epoch 40/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2107 - loss: 2.3771  
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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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Epoch 52/132

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[1m330/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2211 - loss: 2.3327
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2230 - loss: 2.3275 - val_accuracy: 0.3236 - val_loss: 2.2053
Epoch 53/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.3662
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[1m329/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.3093
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[1m519/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.3140
[1m555/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.3143
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2245 - loss: 2.3145 - val_accuracy: 0.3151 - val_loss: 2.2040
Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4238
[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2281 - loss: 2.2879  
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2261 - loss: 2.2877
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Epoch 55/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2258 - loss: 2.2994 - val_accuracy: 0.3192 - val_loss: 2.2043
Epoch 56/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 31ms/step - accuracy: 0.2188 - loss: 2.3036
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2150 - loss: 2.3676  
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[1m551/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2272 - loss: 2.3119
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2273 - loss: 2.3115 - val_accuracy: 0.3073 - val_loss: 2.2104
Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1991
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2339 - loss: 2.2480  
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Epoch 58/132

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Epoch 59/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0938 - loss: 2.2872
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2056 - loss: 2.3016  
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Epoch 60/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3007
[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2316 - loss: 2.3048  
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Epoch 61/132

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Epoch 62/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.3407  
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Epoch 63/132

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Epoch 64/132

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Saved model to disk.

Accuracy capturado en la ejecución 7: 29.01 [%]
F1-score capturado en la ejecución 7: 25.86 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 66/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 775us/step
[1m124/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 817us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 31.13 [%]
Global F1 score (validation) = 26.49 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00710455 0.00994506 0.00530428 ... 0.08634831 0.00692125 0.00218827]
 [0.00515024 0.00669946 0.00396935 ... 0.09350051 0.00508433 0.0017345 ]
 [0.00311227 0.00441594 0.00203052 ... 0.08043869 0.00306854 0.00075213]
 ...
 [0.18125853 0.05477651 0.159316   ... 0.00629044 0.20753665 0.1027421 ]
 [0.17132862 0.06536577 0.1587327  ... 0.0093414  0.18550122 0.09834134]
 [0.17411189 0.05099117 0.15918359 ... 0.0073505  0.19774437 0.11315368]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 29.1 [%]
Global accuracy score (test) = 27.51 [%]
Global F1 score (train) = 25.19 [%]
Global F1 score (test) = 24.53 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.42      0.32       184
       CAMINAR USUAL SPEED       0.06      0.04      0.05       184
            CAMINAR ZIGZAG       0.10      0.11      0.11       184
          DE PIE BARRIENDO       0.29      0.03      0.06       184
   DE PIE DOBLANDO TOALLAS       0.24      0.46      0.32       184
    DE PIE MOVIENDO LIBROS       0.21      0.18      0.19       184
          DE PIE USANDO PC       0.62      0.04      0.08       184
        FASE REPOSO CON K5       0.36      0.76      0.49       184
INCREMENTAL CICLOERGOMETRO       0.41      0.49      0.45       184
           SENTADO LEYENDO       0.44      0.38      0.41       184
         SENTADO USANDO PC       0.06      0.02      0.03       184
      SENTADO VIENDO LA TV       0.35      0.32      0.33       184
   SUBIR Y BAJAR ESCALERAS       0.15      0.38      0.21       184
                    TROTAR       0.76      0.54      0.63       161

                  accuracy                           0.28      2737
                 macro avg       0.29      0.28      0.25      2737
              weighted avg       0.28      0.28      0.24      2737

2025-11-05 17:13:30.205583: 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 17:13:30.217041: 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 00:00:1762359210.230233 3931210 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359210.234413 3931210 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359210.244288 3931210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359210.244308 3931210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359210.244310 3931210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359210.244311 3931210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:13:30.247471: 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 00:00:1762359212.528241 3931210 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359214.695424 3931318 service.cc:152] XLA service 0x74b790011ea0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359214.695453 3931318 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:13:34.738388: 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 00:00:1762359214.977874 3931318 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359217.435896 3931318 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.0980 - loss: 2.7752
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.0980 - loss: 2.7750 - val_accuracy: 0.1830 - val_loss: 2.5179
Epoch 7/132

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Epoch 8/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1077 - loss: 2.6872 - val_accuracy: 0.1904 - val_loss: 2.4956
Epoch 9/132

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[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1168 - loss: 2.6616
[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1169 - loss: 2.6613
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1169 - loss: 2.6612 - val_accuracy: 0.1930 - val_loss: 2.4823
Epoch 10/132

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[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1178 - loss: 2.6578  
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Epoch 11/132

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Epoch 12/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1260 - loss: 2.6027  
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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1419 - loss: 2.5983
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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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Epoch 23/132

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Epoch 24/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1917 - loss: 2.4437 - val_accuracy: 0.2581 - val_loss: 2.3343
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.3385
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[1m503/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1911 - loss: 2.4458
[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1913 - loss: 2.4451
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1914 - loss: 2.4445 - val_accuracy: 0.2642 - val_loss: 2.3237
Epoch 26/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1562 - loss: 2.4924
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1724 - loss: 2.4518  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1706 - loss: 2.4531
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Epoch 27/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1963 - loss: 2.4187 - val_accuracy: 0.2682 - val_loss: 2.3136
Epoch 28/132

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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1990 - loss: 2.4076
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1989 - loss: 2.4076
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1989 - loss: 2.4076 - val_accuracy: 0.2736 - val_loss: 2.3014
Epoch 29/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.5000
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1758 - loss: 2.4422  
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Epoch 30/132

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Epoch 31/132

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Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.3564  
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Epoch 35/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2089 - loss: 2.3778  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2062 - loss: 2.3892 - val_accuracy: 0.2908 - val_loss: 2.2752
Epoch 36/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2315 - loss: 2.3933  
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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2105 - loss: 2.3767
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2102 - loss: 2.3764 - val_accuracy: 0.2825 - val_loss: 2.2701
Epoch 37/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.3621
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1849 - loss: 2.3894  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1907 - loss: 2.3810
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Epoch 38/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2119 - loss: 2.3568 - val_accuracy: 0.2906 - val_loss: 2.2655
Epoch 39/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.2649
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1912 - loss: 2.3650  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1941 - loss: 2.3658
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[1m291/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.3577
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[1m473/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2110 - loss: 2.3567
[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2111 - loss: 2.3568
[1m543/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.3570
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2113 - loss: 2.3571 - val_accuracy: 0.2950 - val_loss: 2.2568
Epoch 40/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5345
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2059 - loss: 2.3737  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2101 - loss: 2.3625
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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 2.1721
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2286 - loss: 2.3450  
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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2607 - loss: 2.3042  
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Epoch 52/132

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Epoch 53/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2157 - loss: 2.3108  
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[1m322/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2192 - loss: 2.3151
[1m354/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2193 - loss: 2.3148
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.3137
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2210 - loss: 2.3136 - val_accuracy: 0.3093 - val_loss: 2.2143
Epoch 54/132

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Epoch 55/132

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Epoch 56/132

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Epoch 57/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2310 - loss: 2.2961 - val_accuracy: 0.3115 - val_loss: 2.2123
Epoch 58/132

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[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2288 - loss: 2.3103
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Epoch 59/132

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Epoch 60/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2273 - loss: 2.3034 - val_accuracy: 0.3232 - val_loss: 2.1967
Epoch 61/132

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Epoch 62/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2215 - loss: 2.2993  
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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2292 - loss: 2.2899
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Epoch 67/132

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Epoch 68/132

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Epoch 69/132

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Epoch 70/132

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[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2333 - loss: 2.2839
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Epoch 71/132

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Epoch 72/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0901
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2573 - loss: 2.2432  
[1m 75/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2529 - loss: 2.2508
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[1m287/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.2655
[1m321/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2423 - loss: 2.2664
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[1m546/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2404 - loss: 2.2683
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2403 - loss: 2.2683 - val_accuracy: 0.3236 - val_loss: 2.1794
Epoch 73/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2924
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2402 - loss: 2.2620  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2383 - loss: 2.2604
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Epoch 74/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2450 - loss: 2.2566 - val_accuracy: 0.3143 - val_loss: 2.1825
Epoch 75/132

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Epoch 76/132

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Epoch 77/132

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Epoch 78/132

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Epoch 79/132

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Epoch 80/132

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Epoch 81/132

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Epoch 82/132

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Epoch 83/132

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Epoch 84/132

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Epoch 85/132

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Epoch 86/132

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Epoch 87/132

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Epoch 88/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.2377
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2431 - loss: 2.2377 - val_accuracy: 0.3448 - val_loss: 2.1459
Epoch 89/132

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Epoch 90/132

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Epoch 91/132

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Epoch 92/132

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Epoch 93/132

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Saved model to disk.

Accuracy capturado en la ejecución 8: 27.51 [%]
F1-score capturado en la ejecución 8: 24.53 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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Global accuracy score (validation) = 33.89 [%]
Global F1 score (validation) = 29.41 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00931361 0.01169918 0.00829143 ... 0.09126991 0.01065148 0.00435855]
 [0.00631206 0.00739816 0.00611816 ... 0.09756923 0.00760089 0.00348042]
 [0.00186509 0.00205506 0.00159914 ... 0.04728644 0.00229991 0.00081064]
 ...
 [0.17759387 0.04078652 0.16868502 ... 0.00492899 0.20059258 0.12771913]
 [0.17927685 0.0567673  0.17655165 ... 0.00520188 0.19084564 0.11123122]
 [0.18235436 0.03574494 0.17405711 ... 0.00304467 0.20899568 0.13371307]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 31.49 [%]
Global accuracy score (test) = 29.52 [%]
Global F1 score (train) = 28.56 [%]
Global F1 score (test) = 26.86 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.39      0.31       184
       CAMINAR USUAL SPEED       0.08      0.04      0.06       184
            CAMINAR ZIGZAG       0.21      0.55      0.30       184
          DE PIE BARRIENDO       0.21      0.11      0.14       184
   DE PIE DOBLANDO TOALLAS       0.27      0.40      0.32       184
    DE PIE MOVIENDO LIBROS       0.34      0.28      0.31       184
          DE PIE USANDO PC       0.20      0.13      0.16       184
        FASE REPOSO CON K5       0.38      0.76      0.51       184
INCREMENTAL CICLOERGOMETRO       0.41      0.52      0.46       184
           SENTADO LEYENDO       0.43      0.39      0.41       184
         SENTADO USANDO PC       0.14      0.04      0.07       184
      SENTADO VIENDO LA TV       0.30      0.22      0.26       184
   SUBIR Y BAJAR ESCALERAS       0.13      0.11      0.12       184
                    TROTAR       0.72      0.52      0.60       161

                  accuracy                           0.30      2737
                 macro avg       0.27      0.30      0.27      2737
              weighted avg       0.27      0.30      0.27      2737

2025-11-05 17:15:31.546590: 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 17:15:31.557990: 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 00:00:1762359331.571246 3941515 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359331.575578 3941515 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359331.585524 3941515 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359331.585545 3941515 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359331.585547 3941515 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359331.585549 3941515 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:15:31.588800: 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 00:00:1762359333.899902 3941515 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359336.107724 3941626 service.cc:152] XLA service 0x7aee18011d30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359336.107752 3941626 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:15:36.150466: 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 00:00:1762359336.388179 3941626 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359338.893700 3941626 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1112 - loss: 2.7165  
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[1m290/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1075 - loss: 2.7065
[1m326/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1075 - loss: 2.7064
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[1m535/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1073 - loss: 2.7059
[1m573/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1073 - loss: 2.7056
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Epoch 8/132

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Saved model to disk.

Accuracy capturado en la ejecución 9: 29.52 [%]
F1-score capturado en la ejecución 9: 26.86 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 849us/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 66/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 773us/step
[1m135/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 750us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 19.04 [%]
Global F1 score (validation) = 8.6 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0422495  0.05960766 0.03197405 ... 0.07915217 0.02934953 0.02516605]
 [0.04033772 0.05769648 0.03111652 ... 0.07996966 0.02725589 0.02402207]
 [0.04327089 0.06088236 0.03306682 ... 0.07898277 0.03132853 0.02611383]
 ...
 [0.10809763 0.05492471 0.10881063 ... 0.05202804 0.1239519  0.09063932]
 [0.10951562 0.0553035  0.1095468  ... 0.05181871 0.12397072 0.09117186]
 [0.10967366 0.05500972 0.11037393 ... 0.0518061  0.12390368 0.091123  ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 16.49 [%]
Global accuracy score (test) = 17.35 [%]
Global F1 score (train) = 7.43 [%]
Global F1 score (test) = 8.78 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.15      0.67      0.25       184
          DE PIE BARRIENDO       0.00      0.00      0.00       184
   DE PIE DOBLANDO TOALLAS       0.29      0.02      0.04       184
    DE PIE MOVIENDO LIBROS       0.14      0.01      0.02       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.14      0.88      0.25       184
INCREMENTAL CICLOERGOMETRO       0.23      0.71      0.35       184
           SENTADO LEYENDO       0.56      0.27      0.36       184
         SENTADO USANDO PC       0.00      0.00      0.00       184
      SENTADO VIENDO LA TV       0.08      0.01      0.02       184
   SUBIR Y BAJAR ESCALERAS       0.00      0.00      0.00       184
                    TROTAR       0.27      0.02      0.03       161

                  accuracy                           0.17      2737
                 macro avg       0.12      0.17      0.09      2737
              weighted avg       0.12      0.17      0.09      2737

2025-11-05 17:16:03.196459: 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 17:16:03.207825: 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 00:00:1762359363.220946 3943300 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359363.224911 3943300 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359363.234928 3943300 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359363.234945 3943300 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359363.234947 3943300 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359363.234948 3943300 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:16:03.238319: 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 00:00:1762359365.500734 3943300 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359367.651321 3943430 service.cc:152] XLA service 0x78d9e8002860 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359367.651349 3943430 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:16:07.694220: 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 00:00:1762359367.925823 3943430 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359370.377495 3943430 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m532/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1077 - loss: 2.6909
[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1077 - loss: 2.6905
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1077 - loss: 2.6904 - val_accuracy: 0.2081 - val_loss: 2.4908
Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1303 - loss: 2.6044  
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[1m536/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1383 - loss: 2.6109
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1385 - loss: 2.6103 - val_accuracy: 0.2015 - val_loss: 2.4369
Epoch 11/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1537 - loss: 2.5654  
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Epoch 12/132

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Epoch 13/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1274 - loss: 2.5621  
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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1818 - loss: 2.4624 - val_accuracy: 0.2408 - val_loss: 2.3539
Epoch 21/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.3599
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1833 - loss: 2.4207  
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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1812 - loss: 2.4547
[1m548/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1814 - loss: 2.4547
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1816 - loss: 2.4545 - val_accuracy: 0.2480 - val_loss: 2.3435
Epoch 22/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 2.2704
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2084 - loss: 2.4038  
[1m 77/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2018 - loss: 2.4255
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Epoch 23/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1866 - loss: 2.4525 - val_accuracy: 0.2432 - val_loss: 2.3373
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2772
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2100 - loss: 2.3725  
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[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.4216
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.4217
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1950 - loss: 2.4217 - val_accuracy: 0.2537 - val_loss: 2.3246
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.4573
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2065 - loss: 2.4010  
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Epoch 26/132

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Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 2.0198
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3663  
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Epoch 28/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2013 - loss: 2.4032 - val_accuracy: 0.2636 - val_loss: 2.2947
Epoch 29/132

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[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2087 - loss: 2.3855
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2086 - loss: 2.3858 - val_accuracy: 0.2752 - val_loss: 2.2792
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.4694
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1953 - loss: 2.3603  
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Epoch 31/132

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Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2021 - loss: 2.3942  
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Epoch 36/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2158 - loss: 2.3473  
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Epoch 37/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2179 - loss: 2.3314  
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[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2145 - loss: 2.3612
[1m574/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2146 - loss: 2.3611
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2146 - loss: 2.3611 - val_accuracy: 0.2893 - val_loss: 2.2439
Epoch 38/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 2.2113
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2115 - loss: 2.3305  
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Epoch 39/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2155 - loss: 2.3504 - val_accuracy: 0.2992 - val_loss: 2.2379
Epoch 40/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2302 - loss: 2.3188  
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[1m295/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.3275
[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.3283
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2214 - loss: 2.3333 - val_accuracy: 0.2916 - val_loss: 2.2379
Epoch 41/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.4707
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2251 - loss: 2.3827  
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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2976
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[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2192 - loss: 2.3290
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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2223 - loss: 2.3217 - val_accuracy: 0.3077 - val_loss: 2.2096
Epoch 50/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2213 - loss: 2.3226 - val_accuracy: 0.3107 - val_loss: 2.2030
Epoch 51/132

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[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2170 - loss: 2.3372
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[1m522/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.3305
[1m561/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2189 - loss: 2.3294
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2190 - loss: 2.3289 - val_accuracy: 0.3006 - val_loss: 2.2038
Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1958
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 2.2339  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2600 - loss: 2.2498
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Epoch 53/132

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Epoch 54/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2284 - loss: 2.3103 - val_accuracy: 0.3061 - val_loss: 2.1924
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 2.4336
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2365 - loss: 2.3050  
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Epoch 56/132

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Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1300
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.2838  
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[1m556/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2282 - loss: 2.2972
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Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1963
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Epoch 59/132

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Epoch 60/132

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Saved model to disk.

Accuracy capturado en la ejecución 10: 17.35 [%]
F1-score capturado en la ejecución 10: 8.78 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m71/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 721us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 68/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 751us/step
[1m133/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 764us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 31.65 [%]
Global F1 score (validation) = 26.08 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00594057 0.00684811 0.00431566 ... 0.07834592 0.00647291 0.00167249]
 [0.00470231 0.00466622 0.0036395  ... 0.078919   0.00520561 0.00162158]
 [0.00301864 0.00347718 0.00206324 ... 0.06954859 0.00341128 0.00069943]
 ...
 [0.16199367 0.05748041 0.15056561 ... 0.01307241 0.16885607 0.10900642]
 [0.1598486  0.05982555 0.14877819 ... 0.01411594 0.16513067 0.10593136]
 [0.16813375 0.05071886 0.15657909 ... 0.00964104 0.18014167 0.1190109 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 28.64 [%]
Global accuracy score (test) = 27.0 [%]
Global F1 score (train) = 23.99 [%]
Global F1 score (test) = 22.38 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.02      0.01      0.01       184
 CAMINAR CON MÓVIL O LIBRO       0.21      0.33      0.26       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.19      0.65      0.30       184
          DE PIE BARRIENDO       0.06      0.02      0.03       184
   DE PIE DOBLANDO TOALLAS       0.26      0.37      0.30       184
    DE PIE MOVIENDO LIBROS       0.25      0.21      0.23       184
          DE PIE USANDO PC       0.24      0.05      0.08       184
        FASE REPOSO CON K5       0.31      0.76      0.44       184
INCREMENTAL CICLOERGOMETRO       0.38      0.56      0.45       184
           SENTADO LEYENDO       0.38      0.39      0.38       184
         SENTADO USANDO PC       0.04      0.01      0.01       184
      SENTADO VIENDO LA TV       0.29      0.16      0.20       184
   SUBIR Y BAJAR ESCALERAS       0.20      0.03      0.05       184
                    TROTAR       0.70      0.55      0.61       161

                  accuracy                           0.27      2737
                 macro avg       0.24      0.27      0.22      2737
              weighted avg       0.23      0.27      0.22      2737

2025-11-05 17:17:29.274747: 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 17:17:29.286204: 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 00:00:1762359449.299253 3950292 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359449.303376 3950292 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359449.313042 3950292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359449.313061 3950292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359449.313063 3950292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359449.313064 3950292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:17:29.316204: 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 00:00:1762359451.601343 3950292 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359453.744580 3950425 service.cc:152] XLA service 0x7a1bc0011060 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359453.744618 3950425 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:17:33.789471: 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 00:00:1762359454.023937 3950425 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359456.482381 3950425 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40:26[0m 4s/step - accuracy: 0.0938 - loss: 4.5523
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[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0652 - loss: 4.2707
[1m109/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0655 - loss: 4.2476
[1m146/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0651 - loss: 4.2299
[1m181/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0650 - loss: 4.2144
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Epoch 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1076 - loss: 2.7009 - val_accuracy: 0.1950 - val_loss: 2.4789
Epoch 8/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1158 - loss: 2.6712 - val_accuracy: 0.2007 - val_loss: 2.4644
Epoch 9/132

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[1m 29/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1008 - loss: 2.6738  
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Epoch 10/132

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Epoch 11/132

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[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1338 - loss: 2.6006
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Epoch 12/132

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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1890 - loss: 2.4580 - val_accuracy: 0.2408 - val_loss: 2.3482
Epoch 22/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1984 - loss: 2.4727  
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Epoch 23/132

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Epoch 24/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1880 - loss: 2.4472 - val_accuracy: 0.2615 - val_loss: 2.3348
Epoch 25/132

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Epoch 26/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1965 - loss: 2.4273 - val_accuracy: 0.2629 - val_loss: 2.3178
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4642
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[1m524/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1951 - loss: 2.4127
[1m562/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.4130
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1950 - loss: 2.4131 - val_accuracy: 0.2740 - val_loss: 2.3098
Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.5756
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Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1946 - loss: 2.4186 - val_accuracy: 0.2648 - val_loss: 2.3036
Epoch 30/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.3688  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.3735
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2028 - loss: 2.3857 - val_accuracy: 0.2716 - val_loss: 2.2999
Epoch 31/132

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Epoch 32/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2043 - loss: 2.4033 - val_accuracy: 0.2762 - val_loss: 2.2898
Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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[1m536/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2042 - loss: 2.3829
[1m571/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2046 - loss: 2.3824
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2047 - loss: 2.3824 - val_accuracy: 0.2827 - val_loss: 2.2789
Epoch 36/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1972 - loss: 2.4019  
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Epoch 37/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2176 - loss: 2.3645 - val_accuracy: 0.2805 - val_loss: 2.2736
Epoch 38/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.3292
[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2265 - loss: 2.3444  
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[1m186/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2229 - loss: 2.3576
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[1m257/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2207 - loss: 2.3593
[1m293/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2199 - loss: 2.3596
[1m329/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2193 - loss: 2.3596
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[1m508/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2173 - loss: 2.3609
[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2169 - loss: 2.3611
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2167 - loss: 2.3613 - val_accuracy: 0.2801 - val_loss: 2.2606
Epoch 39/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.2813
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.3558  
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Epoch 40/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2127 - loss: 2.3592 - val_accuracy: 0.2972 - val_loss: 2.2671
Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2232 - loss: 2.3250 - val_accuracy: 0.3131 - val_loss: 2.2177
Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4744
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2373 - loss: 2.2976  
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Epoch 53/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2360 - loss: 2.3184 - val_accuracy: 0.3024 - val_loss: 2.2104
Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3682
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2359 - loss: 2.3381  
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[1m555/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2300 - loss: 2.3147
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2300 - loss: 2.3145 - val_accuracy: 0.3198 - val_loss: 2.2111
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.5610
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2041 - loss: 2.3640  
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Epoch 56/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2242 - loss: 2.3114 - val_accuracy: 0.3182 - val_loss: 2.2090
Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4821
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[1m296/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.3073
[1m334/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2301 - loss: 2.3071
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Epoch 58/132

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Epoch 59/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2318 - loss: 2.3022 - val_accuracy: 0.3232 - val_loss: 2.2104
Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2400 - loss: 2.2754 - val_accuracy: 0.3250 - val_loss: 2.1847
Epoch 68/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2812 - loss: 2.1680
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Epoch 69/132

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Epoch 70/132

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Epoch 71/132

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Epoch 72/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2426 - loss: 2.2678 - val_accuracy: 0.3302 - val_loss: 2.1740
Epoch 73/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2417 - loss: 2.2647 - val_accuracy: 0.3218 - val_loss: 2.1804
Epoch 74/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.3367
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Epoch 75/132

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Epoch 76/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.3191
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2507 - loss: 2.2452  
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[1m545/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2435 - loss: 2.2504
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2432 - loss: 2.2508
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2431 - loss: 2.2509 - val_accuracy: 0.3349 - val_loss: 2.1728
Epoch 77/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2622 - loss: 2.2413  
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Epoch 78/132

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Epoch 79/132

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Epoch 80/132

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Epoch 81/132

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Epoch 82/132

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Epoch 83/132

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Epoch 84/132

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Epoch 85/132

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Epoch 86/132

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Epoch 87/132

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Epoch 88/132

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Epoch 89/132

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Epoch 90/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.1904  
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Epoch 91/132

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Epoch 92/132

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Epoch 93/132

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Epoch 94/132

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Epoch 95/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2508 - loss: 2.2048  
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[1m545/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2468 - loss: 2.2217
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2470 - loss: 2.2218 - val_accuracy: 0.3425 - val_loss: 2.1511
Epoch 96/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 2.1843
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2485 - loss: 2.2710  
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Epoch 97/132

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Epoch 98/132

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Epoch 99/132

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Epoch 100/132

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Epoch 101/132

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[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 857us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 11: 27.0 [%]
F1-score capturado en la ejecución 11: 22.38 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 67/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 764us/step
[1m133/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 766us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 35.2 [%]
Global F1 score (validation) = 31.76 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00942906 0.01317499 0.00727707 ... 0.08020283 0.0108088  0.00362871]
 [0.00570205 0.00695022 0.00518601 ... 0.10530494 0.00666238 0.00295493]
 [0.00304913 0.00462133 0.00240824 ... 0.07228156 0.0036475  0.00123518]
 ...
 [0.14164174 0.06596462 0.13206239 ... 0.0280314  0.14355935 0.06989133]
 [0.18309386 0.09184475 0.1829784  ... 0.0065812  0.16866226 0.07296615]
 [0.19152592 0.04955123 0.17548916 ... 0.0049354  0.19713391 0.10608511]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 33.42 [%]
Global accuracy score (test) = 28.5 [%]
Global F1 score (train) = 31.44 [%]
Global F1 score (test) = 26.17 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.36      0.30       184
       CAMINAR USUAL SPEED       0.05      0.03      0.04       184
            CAMINAR ZIGZAG       0.23      0.47      0.31       184
          DE PIE BARRIENDO       0.06      0.03      0.04       184
   DE PIE DOBLANDO TOALLAS       0.23      0.34      0.27       184
    DE PIE MOVIENDO LIBROS       0.27      0.22      0.24       184
          DE PIE USANDO PC       0.04      0.01      0.02       184
        FASE REPOSO CON K5       0.39      0.76      0.52       184
INCREMENTAL CICLOERGOMETRO       0.45      0.50      0.47       184
           SENTADO LEYENDO       0.48      0.40      0.43       184
         SENTADO USANDO PC       0.16      0.10      0.13       184
      SENTADO VIENDO LA TV       0.31      0.29      0.30       184
   SUBIR Y BAJAR ESCALERAS       0.20      0.28      0.24       184
                    TROTAR       0.82      0.52      0.63       161

                  accuracy                           0.28      2737
                 macro avg       0.26      0.29      0.26      2737
              weighted avg       0.26      0.28      0.26      2737

2025-11-05 17:19:39.290698: 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 17:19:39.301975: 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 00:00:1762359579.315093 3961406 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359579.319311 3961406 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359579.329213 3961406 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359579.329232 3961406 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359579.329235 3961406 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359579.329237 3961406 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:19:39.332477: 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 00:00:1762359581.616049 3961406 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359583.782690 3961513 service.cc:152] XLA service 0x7f1ae400f2a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359583.782722 3961513 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:19:43.826039: 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 00:00:1762359584.071294 3961513 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359586.511664 3961513 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.0994 - loss: 2.8381 - val_accuracy: 0.2180 - val_loss: 2.4519
Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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[1m532/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1184 - loss: 2.6652
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Epoch 9/132

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Epoch 10/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1409 - loss: 2.6040 - val_accuracy: 0.2170 - val_loss: 2.4268
Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1559 - loss: 2.5484 - val_accuracy: 0.2327 - val_loss: 2.3924
Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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Epoch 23/132

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Epoch 24/132

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[1m536/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1908 - loss: 2.4302
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1910 - loss: 2.4301 - val_accuracy: 0.2509 - val_loss: 2.3207
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.4494
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2014 - loss: 2.4481  
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Epoch 26/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1952 - loss: 2.4227 - val_accuracy: 0.2742 - val_loss: 2.3120
Epoch 27/132

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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1896 - loss: 2.4158
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.1898 - loss: 2.4154
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Epoch 28/132

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Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2019 - loss: 2.4077 - val_accuracy: 0.2946 - val_loss: 2.2964
Epoch 30/132

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Epoch 31/132

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Epoch 32/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2027 - loss: 2.3945  
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Epoch 33/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2084 - loss: 2.4002  
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Epoch 34/132

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Epoch 35/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2249 - loss: 2.3499  
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[1m548/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2106 - loss: 2.3800
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2104 - loss: 2.3801 - val_accuracy: 0.2912 - val_loss: 2.2728
Epoch 36/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0938 - loss: 2.4321
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1950 - loss: 2.3778  
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Epoch 37/132

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Epoch 38/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.3239  
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[1m527/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2103 - loss: 2.3635
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Epoch 39/132

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[1m 40/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2270 - loss: 2.3592  
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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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[1m 30/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2143 - loss: 2.3227  
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[1m540/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2188 - loss: 2.3427
[1m576/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2189 - loss: 2.3428
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2189 - loss: 2.3428 - val_accuracy: 0.2992 - val_loss: 2.2447
Epoch 44/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1870
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2306 - loss: 2.3052  
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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2207 - loss: 2.3299 - val_accuracy: 0.3097 - val_loss: 2.2169
Epoch 49/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2106 - loss: 2.3678  
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[1m216/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2194 - loss: 2.3382
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[1m286/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2201 - loss: 2.3356
[1m321/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2202 - loss: 2.3349
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[1m576/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2214 - loss: 2.3315
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Epoch 50/132

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Epoch 51/132

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Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5471
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2309 - loss: 2.3501  
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Epoch 53/132

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Epoch 54/132

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[1m528/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2270 - loss: 2.3037
[1m564/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2270 - loss: 2.3041
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2271 - loss: 2.3042 - val_accuracy: 0.3171 - val_loss: 2.2016
Epoch 55/132

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Epoch 56/132

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[1m574/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2303 - loss: 2.3088
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2303 - loss: 2.3088 - val_accuracy: 0.3224 - val_loss: 2.1995
Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.3252
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2339 - loss: 2.2749  
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[1m336/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2254 - loss: 2.3051
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2273 - loss: 2.3049 - val_accuracy: 0.3238 - val_loss: 2.1994
Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 2.0113
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2310 - loss: 2.2849  
[1m 77/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2239 - loss: 2.3224
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Saved model to disk.

Accuracy capturado en la ejecución 12: 28.5 [%]
F1-score capturado en la ejecución 12: 26.17 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 909us/step
[1m128/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 800us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 32.12 [%]
Global F1 score (validation) = 26.43 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.01016112 0.0130533  0.00780758 ... 0.08910362 0.01054562 0.00271469]
 [0.00940814 0.01113873 0.00762411 ... 0.09982663 0.00979905 0.00306721]
 [0.0042783  0.00561422 0.00311719 ... 0.07969607 0.00458109 0.00092772]
 ...
 [0.1596118  0.0579287  0.14739649 ... 0.0123196  0.17132586 0.12070666]
 [0.15630433 0.06727298 0.15323943 ... 0.0121029  0.16441786 0.11819343]
 [0.16240168 0.05410401 0.14887933 ... 0.01072639 0.1758606  0.12680995]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 29.32 [%]
Global accuracy score (test) = 29.74 [%]
Global F1 score (train) = 24.44 [%]
Global F1 score (test) = 24.55 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.59      0.05      0.10       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.45      0.33       184
       CAMINAR USUAL SPEED       0.07      0.03      0.04       184
            CAMINAR ZIGZAG       0.20      0.64      0.31       184
          DE PIE BARRIENDO       0.09      0.02      0.03       184
   DE PIE DOBLANDO TOALLAS       0.24      0.43      0.31       184
    DE PIE MOVIENDO LIBROS       0.25      0.24      0.25       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.36      0.76      0.49       184
INCREMENTAL CICLOERGOMETRO       0.39      0.54      0.46       184
           SENTADO LEYENDO       0.38      0.39      0.38       184
         SENTADO USANDO PC       0.14      0.02      0.04       184
      SENTADO VIENDO LA TV       0.37      0.36      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.00      0.00      0.00       184
                    TROTAR       0.64      0.55      0.60       161

                  accuracy                           0.30      2737
                 macro avg       0.27      0.30      0.25      2737
              weighted avg       0.26      0.30      0.24      2737

2025-11-05 17:21:03.665944: 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 17:21:03.677301: 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 00:00:1762359663.690404 3968206 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359663.694631 3968206 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359663.704354 3968206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359663.704372 3968206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359663.704374 3968206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359663.704375 3968206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:21:03.707558: 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 00:00:1762359665.983550 3968206 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359668.176539 3968317 service.cc:152] XLA service 0x77b3a4010680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359668.176596 3968317 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:21:08.221904: 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 00:00:1762359668.468815 3968317 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359670.953281 3968317 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1121 - loss: 2.6953 - val_accuracy: 0.2102 - val_loss: 2.4594
Epoch 8/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1380 - loss: 2.6683  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1253 - loss: 2.6666
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[1m214/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1169 - loss: 2.6664
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Epoch 9/132

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Epoch 10/132

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Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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[1m529/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1543 - loss: 2.5503
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1550 - loss: 2.5495 - val_accuracy: 0.2158 - val_loss: 2.3875
Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1868 - loss: 2.4626 - val_accuracy: 0.2408 - val_loss: 2.3375
Epoch 21/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2188 - loss: 2.2242
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1893 - loss: 2.4242  
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[1m298/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1822 - loss: 2.4515
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[1m551/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1837 - loss: 2.4514
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1839 - loss: 2.4514 - val_accuracy: 0.2404 - val_loss: 2.3368
Epoch 22/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5035
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1715 - loss: 2.4582  
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[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1873 - loss: 2.4511
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1873 - loss: 2.4510 - val_accuracy: 0.2396 - val_loss: 2.3246
Epoch 23/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 2.2886
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.4477  
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1957 - loss: 2.4533
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[1m329/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.4461
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[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1925 - loss: 2.4427
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1925 - loss: 2.4422 - val_accuracy: 0.2607 - val_loss: 2.3208
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3125 - loss: 2.4554
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2110 - loss: 2.4349  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.4325
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[1m140/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.4300
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[1m212/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1982 - loss: 2.4261
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[1m281/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1972 - loss: 2.4246
[1m317/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1969 - loss: 2.4240
[1m351/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1968 - loss: 2.4236
[1m387/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1969 - loss: 2.4229
[1m419/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.4225
[1m456/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.4224
[1m488/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.4224
[1m525/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1969 - loss: 2.4224
[1m558/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1968 - loss: 2.4226
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1967 - loss: 2.4228 - val_accuracy: 0.2474 - val_loss: 2.3132
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.0625 - loss: 2.9167
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1973 - loss: 2.4565  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1998 - loss: 2.4299
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[1m251/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2011 - loss: 2.4232
[1m287/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2013 - loss: 2.4224
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[1m359/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.4220
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Epoch 26/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1958 - loss: 2.4162 - val_accuracy: 0.2712 - val_loss: 2.3058
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.5668
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1790 - loss: 2.4649  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1882 - loss: 2.4483
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[1m246/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1999 - loss: 2.4275
[1m281/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2001 - loss: 2.4264
[1m317/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2003 - loss: 2.4254
[1m350/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2005 - loss: 2.4243
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[1m492/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2013 - loss: 2.4203
[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2016 - loss: 2.4194
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2017 - loss: 2.4186
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Epoch 28/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1986 - loss: 2.4237 - val_accuracy: 0.2694 - val_loss: 2.2975
Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2090 - loss: 2.3939 - val_accuracy: 0.2789 - val_loss: 2.2902
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.5265
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1999 - loss: 2.4102  
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Epoch 31/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2022 - loss: 2.4018 - val_accuracy: 0.2573 - val_loss: 2.2895
Epoch 32/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1859 - loss: 2.3990  
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[1m434/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1990 - loss: 2.3929
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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1998 - loss: 2.3928
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2001 - loss: 2.3928
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2001 - loss: 2.3928 - val_accuracy: 0.2640 - val_loss: 2.2856
Epoch 33/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.5112
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1906 - loss: 2.4111  
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Epoch 34/132

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Epoch 35/132

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Epoch 36/132

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Epoch 37/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2130 - loss: 2.3718 - val_accuracy: 0.2863 - val_loss: 2.2560
Epoch 38/132

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Epoch 39/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2154 - loss: 2.3603 - val_accuracy: 0.2916 - val_loss: 2.2492
Epoch 40/132

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[1m 30/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2085 - loss: 2.3624  
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[1m535/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2149 - loss: 2.3509
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2150 - loss: 2.3508 - val_accuracy: 0.2946 - val_loss: 2.2483
Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2240 - loss: 2.3314 - val_accuracy: 0.3087 - val_loss: 2.2284
Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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[1m319/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.3091
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Epoch 52/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2215 - loss: 2.2923  
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Epoch 53/132

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Epoch 54/132

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Epoch 55/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2250 - loss: 2.3073 - val_accuracy: 0.3171 - val_loss: 2.2058
Epoch 56/132

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[1m508/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2302 - loss: 2.2891
[1m540/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2303 - loss: 2.2897
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2304 - loss: 2.2902
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2304 - loss: 2.2903 - val_accuracy: 0.3107 - val_loss: 2.2086
Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.3762
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2326 - loss: 2.2769  
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Epoch 58/132

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Epoch 59/132

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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2087 - loss: 2.3329  
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Epoch 63/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3943
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2383 - loss: 2.2283  
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Epoch 64/132

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Epoch 65/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2263 - loss: 2.3119  
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[1m294/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2290 - loss: 2.2899
[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2293 - loss: 2.2894
[1m369/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2296 - loss: 2.2887
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Epoch 66/132

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[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 771us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 13: 29.74 [%]
F1-score capturado en la ejecución 13: 24.55 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 63/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 813us/step
[1m130/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 785us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 32.32 [%]
Global F1 score (validation) = 28.12 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00682552 0.00887704 0.00546435 ... 0.07987172 0.00788638 0.00211148]
 [0.00605038 0.00788519 0.00520466 ... 0.09886355 0.00691659 0.00217155]
 [0.00322133 0.00423075 0.00251504 ... 0.07814325 0.00378405 0.00091685]
 ...
 [0.08080018 0.05492192 0.07754651 ... 0.07202701 0.08430725 0.03901942]
 [0.09364279 0.07109381 0.09134673 ... 0.05389093 0.09631164 0.05179661]
 [0.15590188 0.06024696 0.15018718 ... 0.01625003 0.16932566 0.0944019 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 29.57 [%]
Global accuracy score (test) = 27.99 [%]
Global F1 score (train) = 25.53 [%]
Global F1 score (test) = 23.93 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.33      0.28       184
       CAMINAR USUAL SPEED       0.03      0.02      0.02       184
            CAMINAR ZIGZAG       0.16      0.17      0.17       184
          DE PIE BARRIENDO       0.00      0.00      0.00       184
   DE PIE DOBLANDO TOALLAS       0.25      0.52      0.34       184
    DE PIE MOVIENDO LIBROS       0.32      0.25      0.28       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.35      0.76      0.48       184
INCREMENTAL CICLOERGOMETRO       0.38      0.59      0.46       184
           SENTADO LEYENDO       0.39      0.39      0.39       184
         SENTADO USANDO PC       0.10      0.04      0.06       184
      SENTADO VIENDO LA TV       0.28      0.19      0.23       184
   SUBIR Y BAJAR ESCALERAS       0.18      0.43      0.26       184
                    TROTAR       0.76      0.55      0.64       161

                  accuracy                           0.28      2737
                 macro avg       0.23      0.28      0.24      2737
              weighted avg       0.22      0.28      0.24      2737

2025-11-05 17:22:36.473547: 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 17:22:36.484688: 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 00:00:1762359756.497723 3975785 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359756.501873 3975785 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359756.511585 3975785 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359756.511602 3975785 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359756.511611 3975785 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359756.511612 3975785 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:22:36.514773: 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 00:00:1762359758.789406 3975785 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359760.953758 3975917 service.cc:152] XLA service 0x7bc90c023b00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359760.953788 3975917 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:22:40.998114: 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 00:00:1762359761.229774 3975917 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359763.665475 3975917 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1406 - loss: 2.6208  
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[1m326/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1309 - loss: 2.6175
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[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1329 - loss: 2.6155
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Epoch 11/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1287 - loss: 2.6363  
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Epoch 12/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1512 - loss: 2.5672 - val_accuracy: 0.2023 - val_loss: 2.4134
Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1800 - loss: 2.4390 - val_accuracy: 0.2365 - val_loss: 2.3245
Epoch 23/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3438 - loss: 2.3855
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[1m535/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1853 - loss: 2.4372
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1856 - loss: 2.4371
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1856 - loss: 2.4371 - val_accuracy: 0.2490 - val_loss: 2.3274
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1520
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1958 - loss: 2.4159  
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Epoch 25/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1928 - loss: 2.4192 - val_accuracy: 0.2436 - val_loss: 2.3072
Epoch 26/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0625 - loss: 2.5634
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1897 - loss: 2.4365  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1902 - loss: 2.4221
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[1m522/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.4157
[1m556/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1953 - loss: 2.4155
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1954 - loss: 2.4155 - val_accuracy: 0.2742 - val_loss: 2.2984
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3801
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2175 - loss: 2.3849  
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Epoch 28/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1970 - loss: 2.4093 - val_accuracy: 0.2698 - val_loss: 2.2972
Epoch 29/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.4193  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1979 - loss: 2.4048 - val_accuracy: 0.2756 - val_loss: 2.2896
Epoch 30/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2034 - loss: 2.4116  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2021 - loss: 2.4058
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2025 - loss: 2.3951 - val_accuracy: 0.2682 - val_loss: 2.2792
Epoch 31/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.4841
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1979 - loss: 2.4016  
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2024 - loss: 2.4030
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2025 - loss: 2.4023
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2026 - loss: 2.4020 - val_accuracy: 0.2771 - val_loss: 2.2804
Epoch 32/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.5534
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2029 - loss: 2.4348  
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Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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Epoch 36/132

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Epoch 37/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1250 - loss: 2.6540
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Epoch 38/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.3244  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2169 - loss: 2.3610 - val_accuracy: 0.2839 - val_loss: 2.2499
Epoch 39/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 2.2954
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2504 - loss: 2.3254  
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[1m309/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2194 - loss: 2.3586
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[1m525/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2178 - loss: 2.3559
[1m559/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2178 - loss: 2.3556
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2178 - loss: 2.3554 - val_accuracy: 0.2805 - val_loss: 2.2503
Epoch 40/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.4299
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2077 - loss: 2.3372  
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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2317 - loss: 2.2588  
[1m 67/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2324 - loss: 2.2733
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[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2275 - loss: 2.3049
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2274 - loss: 2.3051 - val_accuracy: 0.3125 - val_loss: 2.2212
Epoch 51/132

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Epoch 52/132

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[1m530/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2244 - loss: 2.3179
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2244 - loss: 2.3174 - val_accuracy: 0.2974 - val_loss: 2.2114
Epoch 53/132

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[1m317/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2219 - loss: 2.3348
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[1m532/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2248 - loss: 2.3245
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Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3581
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Epoch 55/132

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Epoch 56/132

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Epoch 57/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2298 - loss: 2.3016 - val_accuracy: 0.3161 - val_loss: 2.2051
Epoch 58/132

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[1m509/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2360 - loss: 2.2982
[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2360 - loss: 2.2983
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2360 - loss: 2.2984 - val_accuracy: 0.3171 - val_loss: 2.2063
Epoch 59/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.4121
[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2274 - loss: 2.2931  
[1m 79/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2325 - loss: 2.2997
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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2198 - loss: 2.3003  
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[1m219/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.2866
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Epoch 68/132

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Epoch 69/132

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Epoch 70/132

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Epoch 71/132

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[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2407 - loss: 2.2660
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2418 - loss: 2.2683 - val_accuracy: 0.3248 - val_loss: 2.1790

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[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 764us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 14: 27.99 [%]
F1-score capturado en la ejecución 14: 23.93 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 791us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 66/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 776us/step
[1m136/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 749us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 32.48 [%]
Global F1 score (validation) = 27.07 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0065193  0.00863328 0.00504149 ... 0.08637612 0.0073602  0.00196563]
 [0.0076817  0.00919348 0.00620869 ... 0.10014796 0.00859884 0.00265336]
 [0.00276935 0.00408481 0.00199621 ... 0.07616896 0.00336207 0.00067469]
 ...
 [0.16135852 0.0543406  0.14755921 ... 0.01347526 0.16972393 0.11639608]
 [0.16307594 0.0597082  0.15678258 ... 0.01055147 0.16705297 0.1252893 ]
 [0.16943675 0.04663889 0.1571052  ... 0.00784666 0.18098919 0.13785578]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 30.17 [%]
Global accuracy score (test) = 28.21 [%]
Global F1 score (train) = 25.96 [%]
Global F1 score (test) = 24.29 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.10      0.02      0.04       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.39      0.30       184
       CAMINAR USUAL SPEED       0.03      0.02      0.02       184
            CAMINAR ZIGZAG       0.20      0.65      0.30       184
          DE PIE BARRIENDO       0.09      0.02      0.03       184
   DE PIE DOBLANDO TOALLAS       0.21      0.43      0.29       184
    DE PIE MOVIENDO LIBROS       0.24      0.21      0.23       184
          DE PIE USANDO PC       0.44      0.07      0.11       184
        FASE REPOSO CON K5       0.37      0.76      0.49       184
INCREMENTAL CICLOERGOMETRO       0.44      0.46      0.45       184
           SENTADO LEYENDO       0.40      0.38      0.39       184
         SENTADO USANDO PC       0.26      0.08      0.12       184
      SENTADO VIENDO LA TV       0.31      0.24      0.27       184
   SUBIR Y BAJAR ESCALERAS       0.00      0.00      0.00       184
                    TROTAR       0.70      0.53      0.60       161

                  accuracy                           0.28      2737
                 macro avg       0.27      0.28      0.24      2737
              weighted avg       0.26      0.28      0.24      2737

2025-11-05 17:24:14.931083: 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 17:24:14.942514: 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 00:00:1762359854.955888 3983873 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359854.960143 3983873 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359854.970044 3983873 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359854.970063 3983873 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359854.970065 3983873 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359854.970067 3983873 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:24:14.973332: 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 00:00:1762359857.256159 3983873 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359859.438281 3984005 service.cc:152] XLA service 0x78e11400fa70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359859.438312 3984005 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:24:19.480503: 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 00:00:1762359859.721183 3984005 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359862.153544 3984005 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 415ms/step
[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 848us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 15: 28.21 [%]
F1-score capturado en la ejecución 15: 24.29 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 849us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 62/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 823us/step
[1m132/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 767us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 20.25 [%]
Global F1 score (validation) = 10.44 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.03457135 0.06724465 0.0357827  ... 0.06741064 0.02929934 0.02595098]
 [0.03345761 0.06642325 0.03638842 ... 0.06935502 0.02938511 0.02593806]
 [0.0391239  0.06850383 0.03702971 ... 0.06674794 0.03165881 0.0275619 ]
 ...
 [0.10909018 0.06193414 0.10766454 ... 0.05072671 0.11867366 0.09539007]
 [0.10746782 0.06224525 0.10666037 ... 0.05114068 0.11880922 0.09646476]
 [0.1079995  0.0612973  0.10863893 ... 0.04998275 0.11892693 0.09612661]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 17.66 [%]
Global accuracy score (test) = 16.88 [%]
Global F1 score (train) = 9.05 [%]
Global F1 score (test) = 9.15 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.02      0.04       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.14      0.67      0.23       184
          DE PIE BARRIENDO       0.31      0.02      0.04       184
   DE PIE DOBLANDO TOALLAS       0.29      0.03      0.05       184
    DE PIE MOVIENDO LIBROS       0.07      0.12      0.09       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.17      0.78      0.27       184
INCREMENTAL CICLOERGOMETRO       0.26      0.70      0.37       184
           SENTADO LEYENDO       0.41      0.12      0.19       184
         SENTADO USANDO PC       0.00      0.00      0.00       184
      SENTADO VIENDO LA TV       0.03      0.01      0.01       184
   SUBIR Y BAJAR ESCALERAS       0.00      0.00      0.00       184
                    TROTAR       0.60      0.04      0.07       161

                  accuracy                           0.17      2737
                 macro avg       0.17      0.17      0.09      2737
              weighted avg       0.17      0.17      0.09      2737

2025-11-05 17:24:46.297181: 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 17:24:46.308311: 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 00:00:1762359886.321443 3985705 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762359886.325524 3985705 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762359886.335391 3985705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359886.335410 3985705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359886.335411 3985705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762359886.335412 3985705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:24:46.338693: 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 00:00:1762359888.573079 3985705 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762359890.680232 3985815 service.cc:152] XLA service 0x787cfc003600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762359890.680258 3985815 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:24:50.722614: 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 00:00:1762359890.954272 3985815 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762359893.406532 3985815 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.1545 - loss: 2.5605
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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1762 - loss: 2.5218  
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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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Epoch 23/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1901 - loss: 2.4411 - val_accuracy: 0.2525 - val_loss: 2.3343
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1811
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2213 - loss: 2.4074  
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[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1962 - loss: 2.4351
[1m569/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1959 - loss: 2.4353
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1959 - loss: 2.4354 - val_accuracy: 0.2599 - val_loss: 2.3316
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.2500 - loss: 2.2967
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1839 - loss: 2.4400  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1863 - loss: 2.4364
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Epoch 26/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1975 - loss: 2.4250 - val_accuracy: 0.2646 - val_loss: 2.3227
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0938 - loss: 2.5141
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1927 - loss: 2.4453  
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1958 - loss: 2.4399
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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1930 - loss: 2.4279
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Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4494
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1976 - loss: 2.3947  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1912 - loss: 2.4095
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Epoch 29/132

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Epoch 30/132

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Epoch 31/132

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Epoch 32/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2029 - loss: 2.4012 - val_accuracy: 0.2704 - val_loss: 2.2902
Epoch 33/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1994 - loss: 2.4083  
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Epoch 34/132

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Epoch 35/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.3314  
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[1m525/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2100 - loss: 2.3723
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2097 - loss: 2.3732 - val_accuracy: 0.2841 - val_loss: 2.2820
Epoch 36/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2500 - loss: 2.4257
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2244 - loss: 2.3535  
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Epoch 37/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2144 - loss: 2.3791 - val_accuracy: 0.2813 - val_loss: 2.2789
Epoch 38/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1835 - loss: 2.4029  
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[1m178/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.3790
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[1m321/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2049 - loss: 2.3759
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[1m528/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2073 - loss: 2.3721
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2077 - loss: 2.3717 - val_accuracy: 0.2885 - val_loss: 2.2771
Epoch 39/132

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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.3644  
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[1m522/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2204 - loss: 2.3559
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2203 - loss: 2.3556 - val_accuracy: 0.2998 - val_loss: 2.2609
Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2194 - loss: 2.3272 - val_accuracy: 0.2986 - val_loss: 2.2400
Epoch 51/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2193 - loss: 2.3159 - val_accuracy: 0.3034 - val_loss: 2.2386
Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.4789
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2140 - loss: 2.3451  
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Epoch 53/132

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Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4171
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.3356  
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[1m508/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2238 - loss: 2.3218
[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2238 - loss: 2.3214
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2239 - loss: 2.3211 - val_accuracy: 0.3053 - val_loss: 2.2272
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1875
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2366 - loss: 2.2980  
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Epoch 56/132

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Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 2.3161
[1m 28/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2395 - loss: 2.3286  
[1m 59/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2312 - loss: 2.3106
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[1m314/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2210 - loss: 2.3134
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[1m530/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2210 - loss: 2.3152
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2212 - loss: 2.3153 - val_accuracy: 0.3087 - val_loss: 2.2215
Epoch 58/132

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Epoch 59/132

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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2263 - loss: 2.2999 - val_accuracy: 0.3065 - val_loss: 2.2145
Epoch 63/132

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Epoch 64/132

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Epoch 65/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 2.1474
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Epoch 66/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.5380
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Epoch 67/132

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Epoch 68/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1786
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2309 - loss: 2.3070  
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Epoch 69/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 26ms/step - accuracy: 0.3750 - loss: 1.9901
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2600 - loss: 2.2299  
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Epoch 70/132

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Epoch 71/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2364 - loss: 2.2812  
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Epoch 72/132

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Epoch 73/132

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Epoch 74/132

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Epoch 75/132

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Epoch 76/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.1987  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2388 - loss: 2.2662 - val_accuracy: 0.3107 - val_loss: 2.2036
Epoch 77/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1979
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2467 - loss: 2.2358  
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Epoch 78/132

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Epoch 79/132

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Epoch 80/132

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Epoch 81/132

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Epoch 82/132

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Epoch 83/132

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Epoch 84/132

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Epoch 85/132

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Epoch 86/132

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Epoch 87/132

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Epoch 88/132

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Epoch 89/132

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Epoch 90/132

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Epoch 91/132

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Epoch 92/132

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Epoch 93/132

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Epoch 94/132

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Epoch 95/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2442 - loss: 2.2598 - val_accuracy: 0.3448 - val_loss: 2.1529
Epoch 96/132

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[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2508 - loss: 2.2331
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Epoch 97/132

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Epoch 98/132

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Epoch 99/132

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Epoch 100/132

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[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 740us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 16.88 [%]
F1-score capturado en la ejecución 16: 9.15 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m485/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 729us/step
[1m550/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 734us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 848us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 74/158[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 690us/step
[1m144/158[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 704us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 33.47 [%]
Global F1 score (validation) = 29.55 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00660278 0.00892582 0.00499479 ... 0.0852799  0.00725088 0.00243618]
 [0.0046534  0.00600943 0.00407318 ... 0.10852967 0.0051234  0.00216568]
 [0.00210257 0.00260902 0.0017334  ... 0.07466588 0.00247146 0.00081255]
 ...
 [0.14580235 0.07263063 0.1313264  ... 0.02211246 0.1465798  0.08672243]
 [0.14757152 0.09281165 0.1418141  ... 0.0162396  0.14131553 0.08663189]
 [0.15863861 0.06643438 0.14322007 ... 0.015305   0.16228692 0.09985602]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 32.48 [%]
Global accuracy score (test) = 27.69 [%]
Global F1 score (train) = 30.08 [%]
Global F1 score (test) = 25.22 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.03      0.01      0.02       184
 CAMINAR CON MÓVIL O LIBRO       0.21      0.32      0.25       184
       CAMINAR USUAL SPEED       0.06      0.03      0.04       184
            CAMINAR ZIGZAG       0.21      0.37      0.27       184
          DE PIE BARRIENDO       0.16      0.04      0.07       184
   DE PIE DOBLANDO TOALLAS       0.22      0.41      0.28       184
    DE PIE MOVIENDO LIBROS       0.14      0.10      0.12       184
          DE PIE USANDO PC       0.20      0.10      0.14       184
        FASE REPOSO CON K5       0.38      0.76      0.51       184
INCREMENTAL CICLOERGOMETRO       0.43      0.47      0.45       184
           SENTADO LEYENDO       0.46      0.39      0.42       184
         SENTADO USANDO PC       0.13      0.04      0.06       184
      SENTADO VIENDO LA TV       0.30      0.30      0.30       184
   SUBIR Y BAJAR ESCALERAS       0.20      0.32      0.24       184
                    TROTAR       0.75      0.53      0.62       161

                  accuracy                           0.28      2737
                 macro avg       0.26      0.28      0.25      2737
              weighted avg       0.25      0.28      0.25      2737

2025-11-05 17:26:54.253836: 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 17:26:54.264997: 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 00:00:1762360014.277974 3996677 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360014.282056 3996677 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360014.291787 3996677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360014.291802 3996677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360014.291804 3996677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360014.291805 3996677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:26:54.294895: 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 00:00:1762360016.572788 3996677 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360018.713594 3996816 service.cc:152] XLA service 0x7d8dd0021940 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360018.713621 3996816 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:26:58.756607: 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 00:00:1762360018.993849 3996816 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360021.474895 3996816 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1058 - loss: 2.7159 - val_accuracy: 0.1793 - val_loss: 2.4837
Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1353 - loss: 2.6167 - val_accuracy: 0.1999 - val_loss: 2.4357
Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.4268
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1959 - loss: 2.4361  
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Epoch 22/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3438 - loss: 2.3008
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2060 - loss: 2.4145  
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[1m295/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1995 - loss: 2.4421
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Epoch 23/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1921 - loss: 2.4318 - val_accuracy: 0.2746 - val_loss: 2.3107
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.5882
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.4365  
[1m 75/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2200 - loss: 2.4270
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[1m217/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2110 - loss: 2.4239
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[1m288/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.4271
[1m323/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.4279
[1m363/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2069 - loss: 2.4287
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[1m470/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2050 - loss: 2.4298
[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2044 - loss: 2.4300
[1m550/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2039 - loss: 2.4300
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2036 - loss: 2.4301 - val_accuracy: 0.2837 - val_loss: 2.3062
Epoch 25/132

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Epoch 26/132

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Epoch 27/132

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Epoch 28/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1970 - loss: 2.4255 - val_accuracy: 0.2813 - val_loss: 2.2876
Epoch 29/132

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[1m555/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1984 - loss: 2.4045
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1986 - loss: 2.4043 - val_accuracy: 0.2897 - val_loss: 2.2801
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2328
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1843 - loss: 2.3937  
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Epoch 31/132

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Epoch 32/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2145 - loss: 2.3871  
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Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2209 - loss: 2.3343  
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Epoch 36/132

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Epoch 37/132

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Epoch 38/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.3646  
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Epoch 39/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2113 - loss: 2.3601 - val_accuracy: 0.2994 - val_loss: 2.2452
Epoch 40/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2322 - loss: 2.3441  
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[1m366/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2162 - loss: 2.3517
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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2159 - loss: 2.3525
[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2160 - loss: 2.3529
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2160 - loss: 2.3531 - val_accuracy: 0.2992 - val_loss: 2.2387
Epoch 41/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2812 - loss: 2.2384
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2267 - loss: 2.3449  
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Epoch 42/132

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Epoch 43/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1846 - loss: 2.3957  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2142 - loss: 2.3526 - val_accuracy: 0.3121 - val_loss: 2.2309
Epoch 44/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.4233
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.3453  
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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2250 - loss: 2.3068 - val_accuracy: 0.3198 - val_loss: 2.2078
Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2800
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2350 - loss: 2.2897  
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Epoch 53/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2298 - loss: 2.3133 - val_accuracy: 0.3240 - val_loss: 2.2058
Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 2.2436
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2537 - loss: 2.2439  
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[1m509/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2356 - loss: 2.2909
[1m545/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2353 - loss: 2.2918
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2351 - loss: 2.2925 - val_accuracy: 0.3232 - val_loss: 2.2036
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1875 - loss: 2.2726
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3015  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2205 - loss: 2.3021
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Epoch 56/132

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Epoch 57/132

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Epoch 58/132

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Epoch 59/132

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Epoch 60/132

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[1m289/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2305 - loss: 2.2931
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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3438 - loss: 2.0812
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Epoch 67/132

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Epoch 68/132

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Epoch 69/132

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Epoch 70/132

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Epoch 71/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0297
[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2381 - loss: 2.2809  
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[1m220/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2305 - loss: 2.2845
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[1m288/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2312 - loss: 2.2814
[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.2808
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[1m435/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2327 - loss: 2.2786
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[1m546/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2340 - loss: 2.2766
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2343 - loss: 2.2762 - val_accuracy: 0.3278 - val_loss: 2.1732
Epoch 72/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.4580
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2089 - loss: 2.3130  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2193 - loss: 2.2816
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Epoch 73/132

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Epoch 74/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.2602  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2134 - loss: 2.2734
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[1m333/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2290 - loss: 2.2708
[1m371/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2299 - loss: 2.2701
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2331 - loss: 2.2680
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2331 - loss: 2.2679 - val_accuracy: 0.3282 - val_loss: 2.1550
Epoch 75/132

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Epoch 76/132

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Epoch 77/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2223 - loss: 2.2681  
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Epoch 78/132

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Epoch 79/132

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Epoch 80/132

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Epoch 81/132

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Epoch 82/132

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Epoch 83/132

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Epoch 84/132

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Epoch 85/132

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Epoch 86/132

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Epoch 87/132

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Epoch 88/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.1817  
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Epoch 89/132

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Epoch 90/132

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Epoch 91/132

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Epoch 92/132

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Epoch 93/132

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Epoch 94/132

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Epoch 95/132

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Epoch 96/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.2728  
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Epoch 97/132

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Epoch 98/132

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Epoch 99/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2516 - loss: 2.2240 - val_accuracy: 0.3409 - val_loss: 2.1385
Epoch 100/132

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Epoch 101/132

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Epoch 102/132

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Epoch 103/132

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Epoch 104/132

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Epoch 105/132

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Epoch 106/132

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Epoch 107/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2503 - loss: 2.2183 - val_accuracy: 0.3474 - val_loss: 2.1287
Epoch 108/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0038
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2786 - loss: 2.1492  
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Epoch 109/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2628 - loss: 2.2032 - val_accuracy: 0.3492 - val_loss: 2.1252
Epoch 110/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2841 - loss: 2.1591  
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[1m322/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2589 - loss: 2.2069
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Epoch 111/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2640 - loss: 2.2355  
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[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 814us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 17: 27.69 [%]
F1-score capturado en la ejecución 17: 25.22 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 813us/step
[1m128/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 792us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 33.79 [%]
Global F1 score (validation) = 29.83 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0094053  0.01347008 0.00780863 ... 0.09104464 0.01124772 0.00422446]
 [0.00679919 0.01001885 0.0061663  ... 0.11239085 0.00816993 0.00358126]
 [0.00340598 0.00409752 0.0028737  ... 0.07341091 0.00418684 0.00179472]
 ...
 [0.17822549 0.06158948 0.16349249 ... 0.0082279  0.18060489 0.093835  ]
 [0.12385115 0.07375994 0.11636701 ... 0.03446333 0.1226505  0.06682739]
 [0.19330908 0.04874239 0.17229427 ... 0.00431665 0.20102419 0.09858556]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 33.75 [%]
Global accuracy score (test) = 28.17 [%]
Global F1 score (train) = 31.68 [%]
Global F1 score (test) = 25.39 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.07      0.02      0.03       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.34      0.28       184
       CAMINAR USUAL SPEED       0.12      0.05      0.07       184
            CAMINAR ZIGZAG       0.20      0.65      0.31       184
          DE PIE BARRIENDO       0.09      0.03      0.05       184
   DE PIE DOBLANDO TOALLAS       0.22      0.32      0.26       184
    DE PIE MOVIENDO LIBROS       0.27      0.23      0.25       184
          DE PIE USANDO PC       0.09      0.04      0.05       184
        FASE REPOSO CON K5       0.39      0.76      0.52       184
INCREMENTAL CICLOERGOMETRO       0.46      0.44      0.45       184
           SENTADO LEYENDO       0.41      0.39      0.40       184
         SENTADO USANDO PC       0.16      0.09      0.11       184
      SENTADO VIENDO LA TV       0.29      0.32      0.30       184
   SUBIR Y BAJAR ESCALERAS       0.13      0.06      0.08       184
                    TROTAR       0.86      0.52      0.65       161

                  accuracy                           0.28      2737
                 macro avg       0.27      0.28      0.25      2737
              weighted avg       0.26      0.28      0.25      2737

2025-11-05 17:29:13.263709: 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 17:29:13.274920: 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 00:00:1762360153.288083 4008739 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360153.291981 4008739 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360153.301888 4008739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360153.301906 4008739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360153.301907 4008739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360153.301908 4008739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:29:13.305049: 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 00:00:1762360155.577487 4008739 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360157.744074 4008867 service.cc:152] XLA service 0x76f594011660 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360157.744100 4008867 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:29:17.787659: 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 00:00:1762360158.028241 4008867 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360160.459226 4008867 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1078 - loss: 2.7198 - val_accuracy: 0.1914 - val_loss: 2.4723
Epoch 8/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1239 - loss: 2.7037  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1154 - loss: 2.7014
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[1m214/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1092 - loss: 2.6939
[1m245/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1091 - loss: 2.6926
[1m281/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1088 - loss: 2.6912
[1m319/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1086 - loss: 2.6899
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Epoch 9/132

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Epoch 10/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1332 - loss: 2.6265 - val_accuracy: 0.2063 - val_loss: 2.4485
Epoch 11/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.5314
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1399 - loss: 2.5923  
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Epoch 12/132

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Epoch 13/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1525 - loss: 2.5742  
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[1m559/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1531 - loss: 2.5675
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1532 - loss: 2.5672 - val_accuracy: 0.2158 - val_loss: 2.3956
Epoch 14/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 29ms/step - accuracy: 0.2500 - loss: 2.4055
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1804 - loss: 2.5096  
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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1780 - loss: 2.4871  
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Epoch 18/132

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Epoch 19/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1866 - loss: 2.4476  
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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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Epoch 23/132

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Epoch 24/132

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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1902 - loss: 2.4346
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1901 - loss: 2.4349
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1901 - loss: 2.4349 - val_accuracy: 0.2646 - val_loss: 2.3264
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2500 - loss: 2.3479
[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2131 - loss: 2.4200  
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Epoch 26/132

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[1m575/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1965 - loss: 2.4171
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1965 - loss: 2.4171 - val_accuracy: 0.2690 - val_loss: 2.3164
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2034
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2053 - loss: 2.3890  
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[1m297/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2011 - loss: 2.4143
[1m334/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2010 - loss: 2.4153
[1m370/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2008 - loss: 2.4159
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[1m478/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2005 - loss: 2.4165
[1m517/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2005 - loss: 2.4166
[1m554/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2004 - loss: 2.4168
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2004 - loss: 2.4169 - val_accuracy: 0.2801 - val_loss: 2.3077
Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4457
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2084 - loss: 2.4030  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2021 - loss: 2.4080
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Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1968 - loss: 2.4026 - val_accuracy: 0.2732 - val_loss: 2.2987
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3641
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1823 - loss: 2.4145  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1899 - loss: 2.4024
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[1m318/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1990 - loss: 2.4020
[1m355/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1996 - loss: 2.4021
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[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2012 - loss: 2.4007
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2012 - loss: 2.4003
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2012 - loss: 2.4003 - val_accuracy: 0.2797 - val_loss: 2.2911
Epoch 31/132

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Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2433 - loss: 2.3693  
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[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.3716
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2128 - loss: 2.3719 - val_accuracy: 0.2905 - val_loss: 2.2693
Epoch 36/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.3426
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2301 - loss: 2.3785  
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Epoch 37/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1906 - loss: 2.4228  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2084 - loss: 2.3720 - val_accuracy: 0.2934 - val_loss: 2.2666
Epoch 38/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3608
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1985 - loss: 2.4036  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2002 - loss: 2.3892
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[1m180/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2092 - loss: 2.3766
[1m220/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2100 - loss: 2.3752
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[1m292/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.3738
[1m330/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2118 - loss: 2.3732
[1m363/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.3725
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[1m437/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2129 - loss: 2.3709
[1m475/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2131 - loss: 2.3703
[1m513/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2133 - loss: 2.3697
[1m551/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2134 - loss: 2.3692
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2135 - loss: 2.3687 - val_accuracy: 0.2984 - val_loss: 2.2628
Epoch 39/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3438 - loss: 2.0845
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.3703  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2245 - loss: 2.3679
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[1m183/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2182 - loss: 2.3651
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Epoch 40/132

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Epoch 41/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.3609  
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Epoch 42/132

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Epoch 43/132

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[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2161 - loss: 2.3486
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2159 - loss: 2.3487
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3487 - val_accuracy: 0.3034 - val_loss: 2.2481
Epoch 44/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.3445
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2199 - loss: 2.3577  
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[1m149/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2215 - loss: 2.3482
[1m185/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2218 - loss: 2.3482
[1m220/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2213 - loss: 2.3488
[1m256/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2207 - loss: 2.3492
[1m294/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2207 - loss: 2.3490
[1m329/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2207 - loss: 2.3488
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[1m549/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2203 - loss: 2.3473
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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2207 - loss: 2.3244
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2208 - loss: 2.3243 - val_accuracy: 0.3032 - val_loss: 2.2247
Epoch 50/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.3503
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2134 - loss: 2.3444  
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Epoch 51/132

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Epoch 52/132

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Epoch 53/132

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Epoch 54/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2237 - loss: 2.3076 - val_accuracy: 0.3133 - val_loss: 2.2145
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.3125 - loss: 2.1036
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2619 - loss: 2.2676  
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[1m561/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2345 - loss: 2.3002
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2344 - loss: 2.3004 - val_accuracy: 0.3131 - val_loss: 2.2191
Epoch 56/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4163
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2271 - loss: 2.3143  
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Epoch 57/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2238 - loss: 2.3038 - val_accuracy: 0.3006 - val_loss: 2.2096
Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 2.3844
[1m 40/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.2516  
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.2614
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[1m214/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2373 - loss: 2.2759
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[1m286/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2349 - loss: 2.2819
[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2338 - loss: 2.2841
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[1m508/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.2899
[1m545/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2305 - loss: 2.2908
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2303 - loss: 2.2915 - val_accuracy: 0.3175 - val_loss: 2.1993
Epoch 59/132

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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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[1m555/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2416 - loss: 2.2814
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2415 - loss: 2.2815 - val_accuracy: 0.3151 - val_loss: 2.2012
Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2115 - loss: 2.3233  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2174 - loss: 2.3161
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[1m536/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2317 - loss: 2.2934
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2319 - loss: 2.2928 - val_accuracy: 0.3308 - val_loss: 2.1893
Epoch 67/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3750 - loss: 2.0849
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Epoch 68/132

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Epoch 69/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2356 - loss: 2.2879  
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Epoch 70/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2259
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2138 - loss: 2.2905  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2212 - loss: 2.2865
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Epoch 71/132

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Epoch 72/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2177 - loss: 2.2903  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2365 - loss: 2.2720 - val_accuracy: 0.3286 - val_loss: 2.1691
Epoch 73/132

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Epoch 74/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2415 - loss: 2.2535 - val_accuracy: 0.3321 - val_loss: 2.1696
Epoch 75/132

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Epoch 76/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2366 - loss: 2.2519 - val_accuracy: 0.3292 - val_loss: 2.1821
Epoch 77/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0893
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.2595  
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[1m298/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2387 - loss: 2.2657
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2380 - loss: 2.2635 - val_accuracy: 0.3256 - val_loss: 2.1799
Epoch 78/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1875 - loss: 2.2600
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2476 - loss: 2.2377  
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[1m508/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2423 - loss: 2.2527
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Epoch 79/132

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Epoch 80/132

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 418ms/step
[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 848us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 28.17 [%]
F1-score capturado en la ejecución 18: 25.39 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 68/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 746us/step
[1m138/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 731us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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Global accuracy score (validation) = 33.79 [%]
Global F1 score (validation) = 29.46 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0066179  0.00877747 0.0050603  ... 0.07699327 0.00727142 0.00224737]
 [0.00676505 0.00811876 0.00568208 ... 0.09151677 0.00761894 0.00289518]
 [0.00253951 0.00330291 0.00192879 ... 0.06219013 0.00289401 0.00082681]
 ...
 [0.16841109 0.0702834  0.1516451  ... 0.0134297  0.17850803 0.09219541]
 [0.14344327 0.0817545  0.13309595 ... 0.02436684 0.13865227 0.08083455]
 [0.16860415 0.06427506 0.1505655  ... 0.01305366 0.1799256  0.09930281]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 30.91 [%]
Global accuracy score (test) = 29.16 [%]
Global F1 score (train) = 27.52 [%]
Global F1 score (test) = 25.67 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.08      0.02      0.03       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.33      0.29       184
       CAMINAR USUAL SPEED       0.03      0.01      0.02       184
            CAMINAR ZIGZAG       0.22      0.60      0.32       184
          DE PIE BARRIENDO       0.22      0.08      0.11       184
   DE PIE DOBLANDO TOALLAS       0.27      0.46      0.34       184
    DE PIE MOVIENDO LIBROS       0.27      0.28      0.27       184
          DE PIE USANDO PC       0.03      0.01      0.01       184
        FASE REPOSO CON K5       0.36      0.76      0.49       184
INCREMENTAL CICLOERGOMETRO       0.40      0.53      0.46       184
           SENTADO LEYENDO       0.44      0.39      0.41       184
         SENTADO USANDO PC       0.14      0.05      0.08       184
      SENTADO VIENDO LA TV       0.23      0.22      0.22       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.15      0.18       184
                    TROTAR       0.75      0.52      0.62       161

                  accuracy                           0.29      2737
                 macro avg       0.26      0.29      0.26      2737
              weighted avg       0.26      0.29      0.25      2737

2025-11-05 17:30:59.462143: 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 17:30:59.473236: 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 00:00:1762360259.486571 4017729 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360259.490539 4017729 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360259.500835 4017729 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360259.500855 4017729 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360259.500857 4017729 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360259.500858 4017729 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:30:59.503995: 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 00:00:1762360261.731976 4017729 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360263.893849 4017857 service.cc:152] XLA service 0x74b4840212f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360263.893879 4017857 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:31:03.942580: 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 00:00:1762360264.173417 4017857 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360266.633365 4017857 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40:39[0m 4s/step - accuracy: 0.0312 - loss: 4.3575
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Epoch 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.0947 - loss: 2.8443 - val_accuracy: 0.1993 - val_loss: 2.5078
Epoch 6/132

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[1m330/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1006 - loss: 2.7816
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[1m550/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1004 - loss: 2.7721
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1005 - loss: 2.7710 - val_accuracy: 0.2075 - val_loss: 2.5065
Epoch 7/132

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Epoch 8/132

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Epoch 9/132

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[1m326/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1201 - loss: 2.6535
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[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1219 - loss: 2.6511
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Epoch 10/132

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Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1931 - loss: 2.5095  
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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1800 - loss: 2.4707 - val_accuracy: 0.2380 - val_loss: 2.3520
Epoch 20/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1751 - loss: 2.4615  
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[1m295/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1835 - loss: 2.4605
[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1836 - loss: 2.4603
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[1m545/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1836 - loss: 2.4598
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1834 - loss: 2.4599 - val_accuracy: 0.2388 - val_loss: 2.3499
Epoch 21/132

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Epoch 22/132

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[1m575/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1911 - loss: 2.4450
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1910 - loss: 2.4450 - val_accuracy: 0.2529 - val_loss: 2.3404
Epoch 23/132

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[1m548/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.4370
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1950 - loss: 2.4369 - val_accuracy: 0.2557 - val_loss: 2.3270
Epoch 24/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1890 - loss: 2.4262  
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1919 - loss: 2.4231
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[1m215/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.4229
[1m251/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1948 - loss: 2.4235
[1m288/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1944 - loss: 2.4241
[1m325/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.4247
[1m357/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.4251
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[1m428/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.4253
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[1m532/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1942 - loss: 2.4259
[1m570/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1942 - loss: 2.4261
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1942 - loss: 2.4261 - val_accuracy: 0.2617 - val_loss: 2.3175
Epoch 25/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.4399
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1886 - loss: 2.3923  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1857 - loss: 2.4012
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[1m183/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1888 - loss: 2.4141
[1m221/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1890 - loss: 2.4159
[1m261/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1893 - loss: 2.4174
[1m297/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1899 - loss: 2.4177
[1m336/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1904 - loss: 2.4180
[1m373/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1908 - loss: 2.4182
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[1m444/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1915 - loss: 2.4182
[1m479/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1918 - loss: 2.4182
[1m515/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1920 - loss: 2.4181
[1m552/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.4180
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1925 - loss: 2.4180 - val_accuracy: 0.2766 - val_loss: 2.3007
Epoch 26/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3940
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2108 - loss: 2.3825  
[1m 68/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2096 - loss: 2.3927
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[1m176/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.3974
[1m214/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2030 - loss: 2.3985
[1m251/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2026 - loss: 2.3997
[1m290/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2024 - loss: 2.4008
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Epoch 27/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2266 - loss: 2.3829  
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[1m534/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1993 - loss: 2.4162
[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1992 - loss: 2.4163
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1991 - loss: 2.4163 - val_accuracy: 0.2684 - val_loss: 2.3003
Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4485
[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1971 - loss: 2.3997  
[1m 66/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1946 - loss: 2.3926
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[1m242/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1964 - loss: 2.3983
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[1m313/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1969 - loss: 2.3998
[1m350/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1973 - loss: 2.4002
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[1m524/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1987 - loss: 2.4020
[1m558/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.4024
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1988 - loss: 2.4025 - val_accuracy: 0.2625 - val_loss: 2.3006
Epoch 29/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.4123
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2202 - loss: 2.3781  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2083 - loss: 2.3905
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Epoch 30/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2022 - loss: 2.3978 - val_accuracy: 0.2760 - val_loss: 2.2813
Epoch 31/132

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[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2078 - loss: 2.4000  
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[1m333/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.3919
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[1m527/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2089 - loss: 2.3910
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2086 - loss: 2.3906 - val_accuracy: 0.2829 - val_loss: 2.2716
Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2266 - loss: 2.3543  
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Epoch 36/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4665
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2142 - loss: 2.3683 - val_accuracy: 0.2903 - val_loss: 2.2553
Epoch 37/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2188 - loss: 2.3208
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2161 - loss: 2.3775  
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Epoch 38/132

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Epoch 39/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.6018
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2124 - loss: 2.3786  
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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2500 - loss: 2.2565
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2351 - loss: 2.3289  
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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2313 - loss: 2.3172 - val_accuracy: 0.3083 - val_loss: 2.2065
Epoch 50/132

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Epoch 51/132

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Epoch 52/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2287 - loss: 2.3157 - val_accuracy: 0.3101 - val_loss: 2.1928
Epoch 53/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3750 - loss: 2.4013
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2398 - loss: 2.3050  
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Epoch 54/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2302 - loss: 2.3067 - val_accuracy: 0.3117 - val_loss: 2.1893
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4073
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.3021  
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[1m330/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.2811
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[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2409 - loss: 2.2872
[1m573/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2407 - loss: 2.2878
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2406 - loss: 2.2878 - val_accuracy: 0.3208 - val_loss: 2.1842
Epoch 56/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 27ms/step - accuracy: 0.1250 - loss: 2.4240
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2121 - loss: 2.3555  
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Epoch 57/132

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Epoch 58/132

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Epoch 59/132

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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2339 - loss: 2.2901
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2343 - loss: 2.2895 - val_accuracy: 0.3258 - val_loss: 2.1813
Epoch 64/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9834
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2722 - loss: 2.2241  
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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0625 - loss: 2.6968
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Epoch 68/132

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Epoch 69/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.2802  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.2751
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[1m289/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2463 - loss: 2.2691
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[1m548/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2441 - loss: 2.2684
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2439 - loss: 2.2682 - val_accuracy: 0.3238 - val_loss: 2.1679
Epoch 70/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2530
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2297 - loss: 2.2749  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2293 - loss: 2.2832
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[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 785us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 19: 29.16 [%]
F1-score capturado en la ejecución 19: 25.67 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 766us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 64/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 804us/step
[1m133/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 765us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 33.89 [%]
Global F1 score (validation) = 29.67 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00477958 0.00717379 0.00426028 ... 0.08679975 0.00568027 0.00145994]
 [0.00437448 0.00533285 0.00426007 ... 0.10169835 0.00537243 0.00171883]
 [0.00161782 0.00259999 0.00139408 ... 0.0779516  0.0020301  0.0003798 ]
 ...
 [0.15088867 0.06468035 0.13880724 ... 0.02163725 0.15635647 0.08438171]
 [0.15914837 0.06741419 0.15335007 ... 0.01545104 0.16308309 0.09161219]
 [0.15788236 0.06060132 0.14262553 ... 0.01826731 0.16487303 0.09159568]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 30.9 [%]
Global accuracy score (test) = 28.97 [%]
Global F1 score (train) = 27.91 [%]
Global F1 score (test) = 25.6 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.34      0.27       184
       CAMINAR USUAL SPEED       0.01      0.01      0.01       184
            CAMINAR ZIGZAG       0.23      0.60      0.33       184
          DE PIE BARRIENDO       0.06      0.02      0.03       184
   DE PIE DOBLANDO TOALLAS       0.24      0.39      0.29       184
    DE PIE MOVIENDO LIBROS       0.24      0.21      0.22       184
          DE PIE USANDO PC       0.26      0.08      0.12       184
        FASE REPOSO CON K5       0.36      0.76      0.49       184
INCREMENTAL CICLOERGOMETRO       0.43      0.49      0.46       184
           SENTADO LEYENDO       0.41      0.39      0.40       184
         SENTADO USANDO PC       0.17      0.04      0.06       184
      SENTADO VIENDO LA TV       0.31      0.33      0.32       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.18      0.20       184
                    TROTAR       0.75      0.54      0.63       161

                  accuracy                           0.29      2737
                 macro avg       0.26      0.29      0.26      2737
              weighted avg       0.26      0.29      0.25      2737

2025-11-05 17:32:36.062547: 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 17:32:36.073591: 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 00:00:1762360356.086468 4025713 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360356.090341 4025713 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360356.100162 4025713 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360356.100177 4025713 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360356.100178 4025713 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360356.100179 4025713 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:32:36.103125: 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 00:00:1762360358.345380 4025713 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360360.499577 4025844 service.cc:152] XLA service 0x7e5aa4022810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360360.499629 4025844 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:32:40.545695: 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 00:00:1762360360.776296 4025844 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360363.211081 4025844 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1155 - loss: 2.7767  
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1089 - loss: 2.7838
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Epoch 7/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1038 - loss: 2.7260 - val_accuracy: 0.1854 - val_loss: 2.4840
Epoch 8/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1015 - loss: 2.6539  
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0989 - loss: 2.6622
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[1m218/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1038 - loss: 2.6743
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[1m292/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1062 - loss: 2.6746
[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1071 - loss: 2.6745
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[1m431/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1092 - loss: 2.6738
[1m470/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1098 - loss: 2.6735
[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1103 - loss: 2.6732
[1m549/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1108 - loss: 2.6728
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1112 - loss: 2.6724 - val_accuracy: 0.1965 - val_loss: 2.4777
Epoch 9/132

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Epoch 10/132

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Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1549 - loss: 2.5622 - val_accuracy: 0.2116 - val_loss: 2.3995
Epoch 14/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0312 - loss: 2.4966
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1480 - loss: 2.5334  
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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1857 - loss: 2.4566 - val_accuracy: 0.2374 - val_loss: 2.3464
Epoch 22/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0625 - loss: 2.4131
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1953 - loss: 2.4172  
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[1m220/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.4392
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[1m292/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1909 - loss: 2.4419
[1m330/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1907 - loss: 2.4425
[1m367/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1904 - loss: 2.4431
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[1m553/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1899 - loss: 2.4444
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1898 - loss: 2.4446 - val_accuracy: 0.2376 - val_loss: 2.3452
Epoch 23/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2080 - loss: 2.4138  
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[1m222/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1996 - loss: 2.4395
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Epoch 24/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1934 - loss: 2.4314 - val_accuracy: 0.2492 - val_loss: 2.3285
Epoch 25/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1717 - loss: 2.4880  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1757 - loss: 2.4781
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Epoch 26/132

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Epoch 27/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1896 - loss: 2.4140 - val_accuracy: 0.2750 - val_loss: 2.3080
Epoch 28/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.5815
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1957 - loss: 2.4200 - val_accuracy: 0.2744 - val_loss: 2.3010
Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1975 - loss: 2.4082 - val_accuracy: 0.2748 - val_loss: 2.2957
Epoch 30/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2168 - loss: 2.3673  
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[1m558/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2042 - loss: 2.3898
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2042 - loss: 2.3899 - val_accuracy: 0.2821 - val_loss: 2.2879
Epoch 31/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4171
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1906 - loss: 2.3948  
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Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1855 - loss: 2.4073  
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Epoch 35/132

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Epoch 36/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1865 - loss: 2.4003  
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[1m530/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2044 - loss: 2.3816
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2049 - loss: 2.3810 - val_accuracy: 0.2859 - val_loss: 2.2637
Epoch 37/132

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Epoch 38/132

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Epoch 39/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2242 - loss: 2.3569  
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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.3844
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Epoch 43/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2189 - loss: 2.3381 - val_accuracy: 0.3107 - val_loss: 2.2388
Epoch 44/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.3525
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2310 - loss: 2.3133  
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[1m562/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2238 - loss: 2.3368
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2238 - loss: 2.3369 - val_accuracy: 0.3051 - val_loss: 2.2366
Epoch 45/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.1562 - loss: 2.3995
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Epoch 46/132

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Epoch 47/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4795
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.3367  
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[1m365/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.3249
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Epoch 48/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.3801
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2165 - loss: 2.3360  
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Epoch 49/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2277 - loss: 2.3336 - val_accuracy: 0.3097 - val_loss: 2.2286
Epoch 50/132

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[1m292/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2236 - loss: 2.3277
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Epoch 51/132

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Epoch 52/132

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Epoch 53/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.3605
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Epoch 54/132

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[1m558/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2265 - loss: 2.3130
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2267 - loss: 2.3128 - val_accuracy: 0.3133 - val_loss: 2.2070
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.2500 - loss: 2.1673
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.2973  
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[1m333/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2339 - loss: 2.3054
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[1m514/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2329 - loss: 2.3057
[1m552/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2327 - loss: 2.3057
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2325 - loss: 2.3056 - val_accuracy: 0.3182 - val_loss: 2.2057
Epoch 56/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 2.1185
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.2757  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.2902
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Epoch 57/132

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Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.2149
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.3287  
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[1m298/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2197 - loss: 2.3142
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[1m523/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.3092
[1m561/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2236 - loss: 2.3086
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2239 - loss: 2.3083 - val_accuracy: 0.3194 - val_loss: 2.2009
Epoch 59/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 2.2938
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2680 - loss: 2.2638  
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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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Epoch 68/132

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Epoch 69/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2420 - loss: 2.2776 - val_accuracy: 0.3401 - val_loss: 2.1751
Epoch 70/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0868
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[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 2.2628
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Epoch 71/132

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Epoch 72/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2438 - loss: 2.2561 - val_accuracy: 0.3315 - val_loss: 2.1729
Epoch 73/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2366 - loss: 2.3144  
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Epoch 74/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2373 - loss: 2.2608 - val_accuracy: 0.3395 - val_loss: 2.1705
Epoch 75/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2124 - loss: 2.2944  
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Epoch 76/132

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Epoch 77/132

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[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2479 - loss: 2.2633
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2478 - loss: 2.2633 - val_accuracy: 0.3443 - val_loss: 2.1577
Epoch 78/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1578
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2290 - loss: 2.2323  
[1m 67/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2362 - loss: 2.2310
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[1m214/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.2414
[1m250/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2419 - loss: 2.2440
[1m287/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.2460
[1m321/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2420 - loss: 2.2470
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Epoch 79/132

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Epoch 80/132

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Epoch 81/132

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Epoch 82/132

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Epoch 83/132

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Epoch 84/132

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Epoch 85/132

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Epoch 86/132

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Epoch 87/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2621 - loss: 2.1785  
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Epoch 88/132

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Epoch 89/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1039
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2322 - loss: 2.1992  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2482 - loss: 2.2408 - val_accuracy: 0.3363 - val_loss: 2.1618
Epoch 90/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0486
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Epoch 91/132

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Epoch 92/132

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Epoch 93/132

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Epoch 94/132

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Epoch 95/132

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Epoch 96/132

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Epoch 97/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2432 - loss: 2.2523 - val_accuracy: 0.3369 - val_loss: 2.1491
Epoch 98/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 2.1221
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Epoch 99/132

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Epoch 100/132

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Epoch 101/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.3125 - loss: 2.3214
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Epoch 102/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2571 - loss: 2.2047 - val_accuracy: 0.3425 - val_loss: 2.1360
Epoch 103/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2566 - loss: 2.1886  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.2004
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[1m186/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2635 - loss: 2.2064
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[1m261/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2626 - loss: 2.2088
[1m299/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2623 - loss: 2.2093
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[1m559/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.2100
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2614 - loss: 2.2102 - val_accuracy: 0.3508 - val_loss: 2.1361

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 423ms/step
[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 788us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 28.97 [%]
F1-score capturado en la ejecución 20: 25.6 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 61/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 837us/step
[1m130/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 778us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 35.08 [%]
Global F1 score (validation) = 31.23 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00821519 0.01053947 0.00636366 ... 0.09415385 0.00879614 0.00313888]
 [0.00493009 0.00632088 0.00421281 ... 0.11329705 0.00556039 0.00218102]
 [0.00300748 0.00407604 0.0023865  ... 0.09128048 0.00341369 0.00108721]
 ...
 [0.13010441 0.08349767 0.12114021 ... 0.03106356 0.1319453  0.06330982]
 [0.10253721 0.08793017 0.09669317 ... 0.04641711 0.09708866 0.05204166]
 [0.16284482 0.06555691 0.15289167 ... 0.01250417 0.17779061 0.0883203 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 33.16 [%]
Global accuracy score (test) = 30.65 [%]
Global F1 score (train) = 30.87 [%]
Global F1 score (test) = 28.13 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.33      0.29       184
       CAMINAR USUAL SPEED       0.05      0.02      0.02       184
            CAMINAR ZIGZAG       0.24      0.49      0.32       184
          DE PIE BARRIENDO       0.13      0.05      0.08       184
   DE PIE DOBLANDO TOALLAS       0.27      0.47      0.34       184
    DE PIE MOVIENDO LIBROS       0.31      0.23      0.26       184
          DE PIE USANDO PC       0.20      0.10      0.14       184
        FASE REPOSO CON K5       0.39      0.76      0.51       184
INCREMENTAL CICLOERGOMETRO       0.43      0.47      0.45       184
           SENTADO LEYENDO       0.45      0.40      0.42       184
         SENTADO USANDO PC       0.17      0.10      0.13       184
      SENTADO VIENDO LA TV       0.28      0.34      0.31       184
   SUBIR Y BAJAR ESCALERAS       0.25      0.32      0.28       184
                    TROTAR       0.84      0.55      0.66       161

                  accuracy                           0.31      2737
                 macro avg       0.28      0.31      0.28      2737
              weighted avg       0.28      0.31      0.28      2737

2025-11-05 17:34:45.314968: 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 17:34:45.326186: 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 00:00:1762360485.339430 4036998 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360485.343564 4036998 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360485.353295 4036998 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360485.353311 4036998 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360485.353313 4036998 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360485.353323 4036998 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:34:45.356433: 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 00:00:1762360487.613353 4036998 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360489.726091 4037129 service.cc:152] XLA service 0x7a38cc00ed60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360489.726137 4037129 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:34:49.776854: 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 00:00:1762360490.004273 4037129 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360492.442224 4037129 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39:59[0m 4s/step - accuracy: 0.0625 - loss: 5.3930
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[1m182/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0658 - loss: 4.3379
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[1m261/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0672 - loss: 4.3097
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Epoch 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.0978 - loss: 2.8501 - val_accuracy: 0.1693 - val_loss: 2.5168
Epoch 6/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1106 - loss: 2.7637  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1091 - loss: 2.7623
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[1m212/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1043 - loss: 2.7615
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[1m287/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1029 - loss: 2.7594
[1m322/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1024 - loss: 2.7585
[1m359/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1020 - loss: 2.7575
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[1m551/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1013 - loss: 2.7531
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1013 - loss: 2.7525 - val_accuracy: 0.2017 - val_loss: 2.5159
Epoch 7/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0312 - loss: 2.7730
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Epoch 8/132

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[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 826us/step 
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Saved model to disk.

Accuracy capturado en la ejecución 21: 30.65 [%]
F1-score capturado en la ejecución 21: 28.13 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m131/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 777us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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Global accuracy score (validation) = 20.37 [%]
Global F1 score (validation) = 10.05 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0457827  0.06923608 0.0442952  ... 0.07015657 0.0372766  0.03976805]
 [0.04568782 0.06891957 0.04369222 ... 0.07092162 0.03705502 0.03962616]
 [0.04654108 0.06983843 0.04460366 ... 0.06961298 0.0379319  0.04029712]
 ...
 [0.1261537  0.0595119  0.10848306 ... 0.04079895 0.11180183 0.10712761]
 [0.1253361  0.06057624 0.10810778 ... 0.0406551  0.1116517  0.10726159]
 [0.12393389 0.06016589 0.10755604 ... 0.04122299 0.11293823 0.10453858]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 17.28 [%]
Global accuracy score (test) = 17.61 [%]
Global F1 score (train) = 8.46 [%]
Global F1 score (test) = 9.08 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.01      0.01      0.01       184
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       184
       CAMINAR USUAL SPEED       0.06      0.01      0.02       184
            CAMINAR ZIGZAG       0.16      0.66      0.25       184
          DE PIE BARRIENDO       0.12      0.01      0.01       184
   DE PIE DOBLANDO TOALLAS       0.07      0.01      0.01       184
    DE PIE MOVIENDO LIBROS       0.35      0.03      0.06       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.16      0.84      0.26       184
INCREMENTAL CICLOERGOMETRO       0.25      0.70      0.37       184
           SENTADO LEYENDO       0.26      0.32      0.29       184
         SENTADO USANDO PC       0.00      0.00      0.00       184
      SENTADO VIENDO LA TV       0.00      0.00      0.00       184
   SUBIR Y BAJAR ESCALERAS       0.00      0.00      0.00       184
                    TROTAR       0.32      0.05      0.09       161

                  accuracy                           0.18      2737
                 macro avg       0.12      0.18      0.09      2737
              weighted avg       0.12      0.18      0.09      2737

2025-11-05 17:35:16.691054: 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 17:35:16.702698: 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 00:00:1762360516.715921 4038786 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360516.720028 4038786 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360516.729832 4038786 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360516.729850 4038786 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360516.729851 4038786 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360516.729853 4038786 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:35:16.732997: 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 00:00:1762360519.010561 4038786 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360521.150950 4038916 service.cc:152] XLA service 0x7aee740240a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360521.150973 4038916 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:35:21.193631: 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 00:00:1762360521.421271 4038916 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360523.921059 4038916 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1104 - loss: 2.6792 - val_accuracy: 0.2043 - val_loss: 2.4842
Epoch 9/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1368 - loss: 2.6433  
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[1m516/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1239 - loss: 2.6430
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1239 - loss: 2.6428 - val_accuracy: 0.2011 - val_loss: 2.4690
Epoch 10/132

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Epoch 11/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1329 - loss: 2.6018 - val_accuracy: 0.2116 - val_loss: 2.4415
Epoch 12/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.5969
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1314 - loss: 2.6086  
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[1m319/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1367 - loss: 2.5931
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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1407 - loss: 2.5891
[1m576/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1412 - loss: 2.5887
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1412 - loss: 2.5887 - val_accuracy: 0.2037 - val_loss: 2.4199
Epoch 13/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.6319
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[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1490 - loss: 2.5865
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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1829 - loss: 2.4686 - val_accuracy: 0.2418 - val_loss: 2.3418
Epoch 21/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1556 - loss: 2.4798  
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[1m508/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1774 - loss: 2.4620
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1779 - loss: 2.4610 - val_accuracy: 0.2464 - val_loss: 2.3387
Epoch 22/132

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[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1887 - loss: 2.4769  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1890 - loss: 2.4605
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[1m142/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1930 - loss: 2.4439
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[1m322/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.4401
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[1m540/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.4391
[1m576/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.4390
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1936 - loss: 2.4390 - val_accuracy: 0.2428 - val_loss: 2.3220
Epoch 23/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.1875 - loss: 2.3166
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2036 - loss: 2.4049  
[1m 76/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1965 - loss: 2.4164
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[1m182/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1946 - loss: 2.4269
[1m219/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1942 - loss: 2.4271
[1m257/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1941 - loss: 2.4273
[1m294/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.4272
[1m328/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.4275
[1m363/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1935 - loss: 2.4277
[1m400/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1934 - loss: 2.4282
[1m435/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1933 - loss: 2.4286
[1m469/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1932 - loss: 2.4290
[1m506/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1931 - loss: 2.4296
[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1930 - loss: 2.4299
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1929 - loss: 2.4302
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1929 - loss: 2.4303 - val_accuracy: 0.2601 - val_loss: 2.3224
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4308
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1738 - loss: 2.4600  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1757 - loss: 2.4631
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Epoch 25/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1933 - loss: 2.4309 - val_accuracy: 0.2690 - val_loss: 2.3031
Epoch 26/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.3894
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2026 - loss: 2.4273  
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2009 - loss: 2.4255
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[1m315/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1987 - loss: 2.4198
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1972 - loss: 2.4199
[1m571/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.4199
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Epoch 27/132

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Epoch 28/132

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[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2033 - loss: 2.4096
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2027 - loss: 2.4084 - val_accuracy: 0.2738 - val_loss: 2.2897
Epoch 29/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2188 - loss: 2.2400
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2125 - loss: 2.3613  
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Epoch 30/132

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Epoch 31/132

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[1m536/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2055 - loss: 2.3902
[1m572/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2055 - loss: 2.3901
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2056 - loss: 2.3900 - val_accuracy: 0.2700 - val_loss: 2.2781
Epoch 32/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.6310
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1934 - loss: 2.4416  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2007 - loss: 2.4184
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Epoch 33/132

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Epoch 34/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2116 - loss: 2.3190  
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Epoch 35/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2068 - loss: 2.3676  
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Epoch 36/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2155 - loss: 2.3574 - val_accuracy: 0.3002 - val_loss: 2.2486
Epoch 37/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2253 - loss: 2.3624  
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[1m328/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.3530
[1m365/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2214 - loss: 2.3533
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[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2191 - loss: 2.3544
[1m575/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2188 - loss: 2.3546
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2188 - loss: 2.3546 - val_accuracy: 0.2926 - val_loss: 2.2421
Epoch 38/132

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Epoch 39/132

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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4070
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Epoch 44/132

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Epoch 45/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 2.2060
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2280 - loss: 2.3467  
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Epoch 46/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.4972
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Epoch 47/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2266 - loss: 2.3311 - val_accuracy: 0.2926 - val_loss: 2.2200
Epoch 48/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2311 - loss: 2.3171  
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[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2324 - loss: 2.3107
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Epoch 49/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 2.3846
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2130 - loss: 2.3547  
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Epoch 50/132

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Epoch 51/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2051 - loss: 2.3414  
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Epoch 52/132

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Epoch 53/132

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Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3750 - loss: 2.3014
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2494 - loss: 2.3012  
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Epoch 55/132

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Epoch 56/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4436
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2195 - loss: 2.3227  
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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2262 - loss: 2.3046
[1m574/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2264 - loss: 2.3043
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2264 - loss: 2.3043 - val_accuracy: 0.3115 - val_loss: 2.1907
Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.2812 - loss: 2.1137
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2398 - loss: 2.2880  
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Epoch 58/132

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Epoch 59/132

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Epoch 60/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2084 - loss: 2.3079  
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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Saved model to disk.

Accuracy capturado en la ejecución 22: 17.61 [%]
F1-score capturado en la ejecución 22: 9.08 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 71/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 716us/step
[1m143/158[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 710us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 32.16 [%]
Global F1 score (validation) = 27.36 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00459487 0.00725381 0.00352565 ... 0.07844585 0.00537582 0.00125197]
 [0.00472076 0.00684956 0.00394461 ... 0.09591743 0.00556974 0.00156982]
 [0.00169472 0.00271654 0.00125046 ... 0.06334423 0.00209667 0.00035381]
 ...
 [0.16168036 0.06175195 0.15239619 ... 0.01386862 0.17264242 0.10724939]
 [0.1512398  0.06896971 0.14300442 ... 0.01973216 0.15690501 0.09456619]
 [0.1700032  0.05514158 0.1608461  ... 0.00920564 0.18582995 0.1190028 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 29.87 [%]
Global accuracy score (test) = 28.9 [%]
Global F1 score (train) = 25.77 [%]
Global F1 score (test) = 24.79 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.43      0.31       184
       CAMINAR USUAL SPEED       0.05      0.03      0.04       184
            CAMINAR ZIGZAG       0.22      0.65      0.32       184
          DE PIE BARRIENDO       0.22      0.01      0.02       184
   DE PIE DOBLANDO TOALLAS       0.20      0.36      0.26       184
    DE PIE MOVIENDO LIBROS       0.27      0.27      0.27       184
          DE PIE USANDO PC       0.19      0.05      0.08       184
        FASE REPOSO CON K5       0.35      0.76      0.48       184
INCREMENTAL CICLOERGOMETRO       0.42      0.48      0.45       184
           SENTADO LEYENDO       0.37      0.36      0.37       184
         SENTADO USANDO PC       0.00      0.00      0.00       184
      SENTADO VIENDO LA TV       0.31      0.30      0.31       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.10      0.14       184
                    TROTAR       0.82      0.56      0.66       161

                  accuracy                           0.29      2737
                 macro avg       0.26      0.29      0.25      2737
              weighted avg       0.26      0.29      0.24      2737

2025-11-05 17:36:45.854678: 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 17:36:45.866102: 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 00:00:1762360605.879427 4046074 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360605.883628 4046074 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360605.893393 4046074 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360605.893408 4046074 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360605.893410 4046074 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360605.893411 4046074 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:36:45.896621: 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 00:00:1762360608.166339 4046074 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360610.329158 4046209 service.cc:152] XLA service 0x7097ac004cd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360610.329197 4046209 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:36:50.373883: 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 00:00:1762360610.610408 4046209 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360613.069755 4046209 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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Epoch 8/132

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Epoch 9/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1191 - loss: 2.6567 - val_accuracy: 0.1805 - val_loss: 2.5081
Epoch 10/132

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[1m292/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1190 - loss: 2.6516
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[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1218 - loss: 2.6459
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1221 - loss: 2.6453 - val_accuracy: 0.1789 - val_loss: 2.4899
Epoch 11/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.6020
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1390 - loss: 2.5927  
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Epoch 12/132

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Epoch 13/132

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[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1434 - loss: 2.5813  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1441 - loss: 2.5855 - val_accuracy: 0.1924 - val_loss: 2.4389
Epoch 14/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1460 - loss: 2.5527  
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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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Epoch 21/132

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Epoch 22/132

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Epoch 23/132

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Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2500 - loss: 2.5204
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1951 - loss: 2.4493  
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[1m470/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.4368
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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1922 - loss: 2.4367
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.1921 - loss: 2.4366
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1921 - loss: 2.4366 - val_accuracy: 0.2754 - val_loss: 2.3194
Epoch 25/132

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Epoch 26/132

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Epoch 27/132

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Epoch 28/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2041 - loss: 2.4134 - val_accuracy: 0.2702 - val_loss: 2.2975
Epoch 29/132

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[1m534/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2004 - loss: 2.4071
[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2005 - loss: 2.4067
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2006 - loss: 2.4066 - val_accuracy: 0.2930 - val_loss: 2.2746
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.5100
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1865 - loss: 2.4124  
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Epoch 31/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1978 - loss: 2.3999 - val_accuracy: 0.2964 - val_loss: 2.2659
Epoch 32/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4445
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.3903  
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[1m323/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2148 - loss: 2.3845
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[1m537/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2128 - loss: 2.3845
[1m572/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2124 - loss: 2.3846
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2124 - loss: 2.3847 - val_accuracy: 0.2889 - val_loss: 2.2667
Epoch 33/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0312 - loss: 2.5552
[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1676 - loss: 2.4259  
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Epoch 34/132

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Epoch 35/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2031 - loss: 2.3856  
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Epoch 36/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2334 - loss: 2.3511  
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Epoch 37/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.3754
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2190 - loss: 2.4108  
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[1m503/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2131 - loss: 2.3694
[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2131 - loss: 2.3687
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2131 - loss: 2.3682 - val_accuracy: 0.2926 - val_loss: 2.2358
Epoch 38/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.2943
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2067 - loss: 2.3791  
[1m 75/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2095 - loss: 2.3784
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Epoch 39/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2044 - loss: 2.3675 - val_accuracy: 0.3008 - val_loss: 2.2303
Epoch 40/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 2.0179
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2361 - loss: 2.3206  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2360 - loss: 2.3302
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[1m140/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2320 - loss: 2.3354
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[1m319/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2258 - loss: 2.3427
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[1m509/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.3467
[1m545/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2219 - loss: 2.3470
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2216 - loss: 2.3472 - val_accuracy: 0.2930 - val_loss: 2.2415
Epoch 41/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0938 - loss: 2.4308
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1833 - loss: 2.3691  
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[1m208/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2087 - loss: 2.3466
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Epoch 42/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2171 - loss: 2.3411 - val_accuracy: 0.2960 - val_loss: 2.2081
Epoch 43/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4787
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2245 - loss: 2.3496  
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Epoch 44/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2141 - loss: 2.3353  
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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.2538
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2019 - loss: 2.3026  
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Epoch 53/132

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Epoch 54/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2300 - loss: 2.2998  
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[1m293/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2326 - loss: 2.2960
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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2320 - loss: 2.2962
[1m548/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.2964
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2322 - loss: 2.2965 - val_accuracy: 0.3234 - val_loss: 2.1778
Epoch 55/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.3952
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.3459  
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Epoch 56/132

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Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2330
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2264 - loss: 2.2757  
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Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.2355
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Epoch 59/132

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Epoch 60/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2362 - loss: 2.2748  
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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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Epoch 68/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.2538  
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Epoch 69/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2823
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[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2500 - loss: 2.2522
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Epoch 70/132

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Epoch 71/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2696 - loss: 2.2409  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2508 - loss: 2.2608 - val_accuracy: 0.3359 - val_loss: 2.1375
Epoch 72/132

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Epoch 73/132

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Epoch 74/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1942 - loss: 2.2838  
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Epoch 75/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2406 - loss: 2.2548 - val_accuracy: 0.3439 - val_loss: 2.1305
Epoch 76/132

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[1m530/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2401 - loss: 2.2753
[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2402 - loss: 2.2743
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2402 - loss: 2.2740 - val_accuracy: 0.3415 - val_loss: 2.1359
Epoch 77/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9747
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2675 - loss: 2.2257  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 2.2487
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Epoch 78/132

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Epoch 79/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2463 - loss: 2.1918  
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[1m298/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2419 - loss: 2.2341
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[1m561/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.2398
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2415 - loss: 2.2400 - val_accuracy: 0.3433 - val_loss: 2.1328
Epoch 80/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2191
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2590 - loss: 2.2395  
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Epoch 81/132

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Epoch 82/132

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Epoch 83/132

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Epoch 84/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2447 - loss: 2.2457 - val_accuracy: 0.3323 - val_loss: 2.1288
Epoch 85/132

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Epoch 86/132

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Epoch 87/132

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Epoch 88/132

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Epoch 89/132

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Epoch 90/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2474 - loss: 2.2418 - val_accuracy: 0.3524 - val_loss: 2.1107
Epoch 91/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4167
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Epoch 92/132

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Epoch 93/132

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Epoch 94/132

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Epoch 95/132

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Epoch 96/132

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Epoch 97/132

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Epoch 98/132

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Epoch 99/132

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Epoch 100/132

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[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2541 - loss: 2.2414
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2527 - loss: 2.2219 - val_accuracy: 0.3462 - val_loss: 2.1022

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[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 811us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 23: 28.9 [%]
F1-score capturado en la ejecución 23: 24.79 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 802us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 62/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 829us/step
[1m135/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 754us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 34.62 [%]
Global F1 score (validation) = 31.01 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00893163 0.01159206 0.00738702 ... 0.08694267 0.01048374 0.00497512]
 [0.00713926 0.00916457 0.00650521 ... 0.10206486 0.00850651 0.00400787]
 [0.00293793 0.00413254 0.00237323 ... 0.07133687 0.00363571 0.00149125]
 ...
 [0.17382985 0.05208469 0.16168295 ... 0.00750753 0.1891024  0.11002097]
 [0.19974539 0.05387893 0.19441614 ... 0.00181871 0.21488912 0.10277957]
 [0.18614228 0.03752513 0.1693237  ... 0.00324783 0.21641016 0.11999843]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 32.9 [%]
Global accuracy score (test) = 27.99 [%]
Global F1 score (train) = 30.69 [%]
Global F1 score (test) = 26.16 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.34      0.29       184
       CAMINAR USUAL SPEED       0.08      0.03      0.05       184
            CAMINAR ZIGZAG       0.15      0.29      0.20       184
          DE PIE BARRIENDO       0.19      0.08      0.11       184
   DE PIE DOBLANDO TOALLAS       0.25      0.37      0.30       184
    DE PIE MOVIENDO LIBROS       0.24      0.21      0.22       184
          DE PIE USANDO PC       0.42      0.10      0.16       184
        FASE REPOSO CON K5       0.33      0.76      0.46       184
INCREMENTAL CICLOERGOMETRO       0.50      0.47      0.49       184
           SENTADO LEYENDO       0.38      0.39      0.38       184
         SENTADO USANDO PC       0.14      0.08      0.10       184
      SENTADO VIENDO LA TV       0.28      0.24      0.26       184
   SUBIR Y BAJAR ESCALERAS       0.17      0.33      0.23       184
                    TROTAR       0.89      0.55      0.68       161

                  accuracy                           0.28      2737
                 macro avg       0.29      0.28      0.26      2737
              weighted avg       0.28      0.28      0.26      2737

2025-11-05 17:38:53.157397: 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 17:38:53.168790: 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 00:00:1762360733.181887 4057046 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360733.185985 4057046 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360733.195688 4057046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360733.195704 4057046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360733.195706 4057046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360733.195707 4057046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:38:53.198870: 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 00:00:1762360735.461683 4057046 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360737.609389 4057179 service.cc:152] XLA service 0x7bd85c007230 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360737.609418 4057179 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:38:57.653185: 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 00:00:1762360737.890348 4057179 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360740.370724 4057179 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1103 - loss: 2.6952
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1105 - loss: 2.6949
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1105 - loss: 2.6949 - val_accuracy: 0.1997 - val_loss: 2.4823
Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1318 - loss: 2.6231 - val_accuracy: 0.2081 - val_loss: 2.4442
Epoch 11/132

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Epoch 12/132

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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1834 - loss: 2.4693 - val_accuracy: 0.2404 - val_loss: 2.3470
Epoch 21/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.3438 - loss: 2.3159
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2043 - loss: 2.4282  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1997 - loss: 2.4331
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[1m289/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1932 - loss: 2.4464
[1m327/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1925 - loss: 2.4477
[1m360/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1920 - loss: 2.4486
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[1m463/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1904 - loss: 2.4506
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[1m532/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1897 - loss: 2.4514
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1895 - loss: 2.4518
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1894 - loss: 2.4519 - val_accuracy: 0.2492 - val_loss: 2.3398
Epoch 22/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2812 - loss: 2.4036
[1m 30/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2017 - loss: 2.3959  
[1m 62/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1922 - loss: 2.4136
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Epoch 23/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1933 - loss: 2.4384 - val_accuracy: 0.2571 - val_loss: 2.3248
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4452
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.4331  
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.4273
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[1m181/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.4347
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[1m290/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1925 - loss: 2.4366
[1m325/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1921 - loss: 2.4369
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[1m506/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1917 - loss: 2.4373
[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1917 - loss: 2.4373
[1m576/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1917 - loss: 2.4374
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Epoch 25/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1969 - loss: 2.4351 - val_accuracy: 0.2716 - val_loss: 2.3098
Epoch 26/132

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[1m528/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1952 - loss: 2.4281
[1m563/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1952 - loss: 2.4278
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1951 - loss: 2.4277 - val_accuracy: 0.2726 - val_loss: 2.3062
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.3988
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2066 - loss: 2.3995  
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[1m530/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2016 - loss: 2.4029
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2015 - loss: 2.4033 - val_accuracy: 0.2706 - val_loss: 2.3009
Epoch 28/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1974 - loss: 2.3915  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2013 - loss: 2.4074 - val_accuracy: 0.2748 - val_loss: 2.2867
Epoch 29/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1875 - loss: 2.3300
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2014 - loss: 2.3949  
[1m 68/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2039 - loss: 2.3953
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[1m180/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1991 - loss: 2.4021
[1m216/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1981 - loss: 2.4045
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[1m356/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.4094
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[1m427/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1972 - loss: 2.4101
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[1m537/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1977 - loss: 2.4102
[1m572/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1977 - loss: 2.4102
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1978 - loss: 2.4102 - val_accuracy: 0.2879 - val_loss: 2.2888
Epoch 30/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.7097
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2168 - loss: 2.4551  
[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2074 - loss: 2.4382
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Epoch 31/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1966 - loss: 2.4011 - val_accuracy: 0.2752 - val_loss: 2.2821
Epoch 32/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1874 - loss: 2.3883  
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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1973 - loss: 2.3902
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1976 - loss: 2.3901 - val_accuracy: 0.2883 - val_loss: 2.2767
Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2001 - loss: 2.3761  
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Epoch 36/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.3287  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2180 - loss: 2.3716 - val_accuracy: 0.2984 - val_loss: 2.2539
Epoch 37/132

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[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2039 - loss: 2.3701
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2041 - loss: 2.3702 - val_accuracy: 0.2934 - val_loss: 2.2614
Epoch 38/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3993
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2075 - loss: 2.3537  
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Epoch 39/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2130 - loss: 2.3596 - val_accuracy: 0.2940 - val_loss: 2.2524
Epoch 40/132

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Epoch 41/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1875 - loss: 2.8002
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1958 - loss: 2.4195  
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Epoch 42/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2181 - loss: 2.3465 - val_accuracy: 0.2982 - val_loss: 2.2376
Epoch 43/132

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Epoch 44/132

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Epoch 45/132

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Epoch 46/132

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Epoch 47/132

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Epoch 48/132

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[1m531/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.3310
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2220 - loss: 2.3311 - val_accuracy: 0.3129 - val_loss: 2.2168
Epoch 49/132

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Epoch 50/132

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Epoch 51/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3438 - loss: 2.1585
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2620 - loss: 2.2587  
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[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2283 - loss: 2.3107
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2281 - loss: 2.3140
[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2280 - loss: 2.3143
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2280 - loss: 2.3145 - val_accuracy: 0.3107 - val_loss: 2.2100
Epoch 52/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.1875 - loss: 2.4125
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2061 - loss: 2.3266  
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Epoch 53/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2268 - loss: 2.3232 - val_accuracy: 0.3167 - val_loss: 2.2120
Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.3348
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2423 - loss: 2.3049  
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[1m527/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.3157
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Epoch 55/132

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Epoch 56/132

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[1m331/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2347 - loss: 2.3065
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2343 - loss: 2.3054 - val_accuracy: 0.3236 - val_loss: 2.1960
Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2812 - loss: 2.2790
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2286 - loss: 2.3695  
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[1m546/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2281 - loss: 2.3113
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2282 - loss: 2.3109 - val_accuracy: 0.3206 - val_loss: 2.1962
Epoch 58/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1866
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2487 - loss: 2.2825  
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[1m147/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2367 - loss: 2.2974
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2323 - loss: 2.2991 - val_accuracy: 0.3238 - val_loss: 2.1982
Epoch 59/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.4062 - loss: 2.0747
[1m 39/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2520 - loss: 2.2600  
[1m 75/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.2811
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[1m181/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2353 - loss: 2.2855
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[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2324 - loss: 2.2888
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[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.2907
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2313 - loss: 2.2908 - val_accuracy: 0.3175 - val_loss: 2.1989

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36s[0m 429ms/step
[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 765us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 24: 27.99 [%]
F1-score capturado en la ejecución 24: 26.16 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 67/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 765us/step
[1m138/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 736us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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Global accuracy score (validation) = 31.75 [%]
Global F1 score (validation) = 26.11 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00396989 0.00723643 0.00329687 ... 0.08608142 0.00482878 0.00124999]
 [0.00465285 0.00711962 0.00404825 ... 0.0979334  0.00552675 0.00172744]
 [0.002003   0.00352872 0.00168588 ... 0.08563626 0.00241268 0.00063677]
 ...
 [0.10971665 0.06098733 0.10063292 ... 0.04685097 0.11355643 0.06269122]
 [0.12873872 0.08726491 0.1196321  ... 0.03094154 0.12373819 0.07296147]
 [0.17025201 0.03906398 0.16100094 ... 0.00451523 0.19817138 0.15153302]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 28.58 [%]
Global accuracy score (test) = 27.88 [%]
Global F1 score (train) = 24.1 [%]
Global F1 score (test) = 23.08 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.05      0.01      0.01       184
 CAMINAR CON MÓVIL O LIBRO       0.23      0.32      0.27       184
       CAMINAR USUAL SPEED       0.02      0.01      0.01       184
            CAMINAR ZIGZAG       0.21      0.62      0.32       184
          DE PIE BARRIENDO       0.00      0.00      0.00       184
   DE PIE DOBLANDO TOALLAS       0.22      0.42      0.29       184
    DE PIE MOVIENDO LIBROS       0.34      0.26      0.29       184
          DE PIE USANDO PC       0.04      0.01      0.02       184
        FASE REPOSO CON K5       0.33      0.76      0.46       184
INCREMENTAL CICLOERGOMETRO       0.37      0.58      0.45       184
           SENTADO LEYENDO       0.36      0.39      0.37       184
         SENTADO USANDO PC       0.12      0.04      0.06       184
      SENTADO VIENDO LA TV       0.33      0.21      0.25       184
   SUBIR Y BAJAR ESCALERAS       0.20      0.04      0.07       184
                    TROTAR       0.64      0.55      0.59       161

                  accuracy                           0.28      2737
                 macro avg       0.23      0.28      0.23      2737
              weighted avg       0.23      0.28      0.23      2737

2025-11-05 17:40:18.370288: 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 17:40:18.381442: 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 00:00:1762360818.394699 4063960 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360818.398854 4063960 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360818.408865 4063960 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360818.408883 4063960 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360818.408884 4063960 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360818.408886 4063960 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:40:18.412220: 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 00:00:1762360820.668432 4063960 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360822.832310 4064089 service.cc:152] XLA service 0x7820c40038f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360822.832337 4064089 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:40:22.875057: 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 00:00:1762360823.111110 4064089 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360825.527532 4064089 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1068 - loss: 2.7752 - val_accuracy: 0.1828 - val_loss: 2.4570
Epoch 7/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0625 - loss: 2.6114
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1021 - loss: 2.6972  
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Epoch 8/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1184 - loss: 2.6678 - val_accuracy: 0.1898 - val_loss: 2.4487
Epoch 9/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1098 - loss: 2.6636  
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[1m537/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1228 - loss: 2.6445
[1m574/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1230 - loss: 2.6441
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1230 - loss: 2.6440 - val_accuracy: 0.1928 - val_loss: 2.4402
Epoch 10/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.6670
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Epoch 11/132

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Epoch 12/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1479 - loss: 2.5639 - val_accuracy: 0.2130 - val_loss: 2.3924
Epoch 13/132

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Epoch 14/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1553 - loss: 2.5401 - val_accuracy: 0.2255 - val_loss: 2.3748
Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m562/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1838 - loss: 2.4592
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1838 - loss: 2.4591 - val_accuracy: 0.2525 - val_loss: 2.3407
Epoch 21/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0938 - loss: 2.6548
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1698 - loss: 2.5058  
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Epoch 22/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.4184  
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[1m523/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1877 - loss: 2.4384
[1m559/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1875 - loss: 2.4385
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1875 - loss: 2.4386 - val_accuracy: 0.2599 - val_loss: 2.3241
Epoch 23/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3125 - loss: 2.3146
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2202 - loss: 2.3970  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2103 - loss: 2.4025
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[1m317/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1947 - loss: 2.4260
[1m353/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1943 - loss: 2.4276
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[1m425/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.4301
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[1m503/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1934 - loss: 2.4316
[1m540/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1933 - loss: 2.4321
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1933 - loss: 2.4326
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1933 - loss: 2.4326 - val_accuracy: 0.2615 - val_loss: 2.3171
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1250 - loss: 2.5571
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1781 - loss: 2.4621  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1811 - loss: 2.4513
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Epoch 25/132

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[1m532/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1994 - loss: 2.4217
[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1992 - loss: 2.4218
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1991 - loss: 2.4218 - val_accuracy: 0.2640 - val_loss: 2.3086
Epoch 26/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2812 - loss: 2.3229
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2001 - loss: 2.4031  
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1986 - loss: 2.4074
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[1m323/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1941 - loss: 2.4094
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[1m551/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.4141
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1937 - loss: 2.4143 - val_accuracy: 0.2627 - val_loss: 2.3084
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.5417
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Epoch 28/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2019 - loss: 2.3986 - val_accuracy: 0.2656 - val_loss: 2.2999
Epoch 29/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4967
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1981 - loss: 2.4153  
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Epoch 30/132

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Epoch 31/132

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Epoch 32/132

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Epoch 33/132

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Epoch 34/132

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Epoch 35/132

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Epoch 36/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2124 - loss: 2.3593  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2111 - loss: 2.3738 - val_accuracy: 0.2819 - val_loss: 2.2616
Epoch 37/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1875 - loss: 2.2411
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1900 - loss: 2.3948  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1955 - loss: 2.3971
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[1m294/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2088 - loss: 2.3862
[1m332/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2095 - loss: 2.3853
[1m369/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.3846
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[1m446/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.3834
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[1m554/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2111 - loss: 2.3816
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2112 - loss: 2.3813 - val_accuracy: 0.2905 - val_loss: 2.2597
Epoch 38/132

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Epoch 39/132

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Epoch 40/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2043 - loss: 2.3772  
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Epoch 41/132

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Epoch 42/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2198 - loss: 2.3435 - val_accuracy: 0.2934 - val_loss: 2.2387
Epoch 43/132

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Epoch 44/132

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Epoch 45/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0625 - loss: 2.3605
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1995 - loss: 2.3342  
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[1m556/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2171 - loss: 2.3339
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2171 - loss: 2.3341 - val_accuracy: 0.2903 - val_loss: 2.2461
Epoch 46/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0938 - loss: 2.7529
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1949 - loss: 2.4090  
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Epoch 47/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2154 - loss: 2.3327 - val_accuracy: 0.2984 - val_loss: 2.2431
Epoch 48/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3468
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2042 - loss: 2.3469  
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[1m546/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2181 - loss: 2.3368
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2182 - loss: 2.3367 - val_accuracy: 0.3103 - val_loss: 2.2216
Epoch 49/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.3438 - loss: 2.1979
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2336 - loss: 2.3759  
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Epoch 50/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2268 - loss: 2.3316 - val_accuracy: 0.3030 - val_loss: 2.2236
Epoch 51/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2500 - loss: 2.3367
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[1m329/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2212 - loss: 2.3324
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Epoch 52/132

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Epoch 53/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2239 - loss: 2.3178 - val_accuracy: 0.3077 - val_loss: 2.2288
Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2504
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Epoch 55/132

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Epoch 56/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2196 - loss: 2.2910  
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[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2206 - loss: 2.3218
[1m574/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2209 - loss: 2.3213
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2210 - loss: 2.3212 - val_accuracy: 0.3155 - val_loss: 2.2195
Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3924
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2367 - loss: 2.3033  
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Epoch 58/132

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Epoch 59/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2519 - loss: 2.2968  
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2305 - loss: 2.3011 - val_accuracy: 0.3103 - val_loss: 2.2069
Epoch 60/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4512
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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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Epoch 68/132

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Epoch 69/132

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[1m 70/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.2955
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[1m532/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2375 - loss: 2.2814
[1m567/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2375 - loss: 2.2813
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2374 - loss: 2.2813 - val_accuracy: 0.3149 - val_loss: 2.1903

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[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 807us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 25: 27.88 [%]
F1-score capturado en la ejecución 25: 23.08 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m478/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 739us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 772us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 54/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 952us/step
[1m119/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 852us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 31.49 [%]
Global F1 score (validation) = 25.59 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00823155 0.01137411 0.00567311 ... 0.07986024 0.00910062 0.00227259]
 [0.00631158 0.00816557 0.00475033 ... 0.08705931 0.00729705 0.00207185]
 [0.00453255 0.00577298 0.00338728 ... 0.07928067 0.00539057 0.00143754]
 ...
 [0.12217213 0.05081977 0.11459707 ... 0.03987911 0.13117883 0.08189539]
 [0.15462859 0.0598116  0.14722492 ... 0.01551928 0.16216396 0.11827793]
 [0.1640314  0.04124956 0.15683395 ... 0.00628256 0.18840727 0.16589868]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 28.85 [%]
Global accuracy score (test) = 27.62 [%]
Global F1 score (train) = 24.03 [%]
Global F1 score (test) = 22.9 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.36      0.30       184
       CAMINAR USUAL SPEED       0.03      0.01      0.02       184
            CAMINAR ZIGZAG       0.21      0.62      0.32       184
          DE PIE BARRIENDO       0.09      0.05      0.07       184
   DE PIE DOBLANDO TOALLAS       0.23      0.39      0.29       184
    DE PIE MOVIENDO LIBROS       0.28      0.28      0.28       184
          DE PIE USANDO PC       0.34      0.07      0.12       184
        FASE REPOSO CON K5       0.35      0.76      0.48       184
INCREMENTAL CICLOERGOMETRO       0.40      0.53      0.46       184
           SENTADO LEYENDO       0.43      0.38      0.40       184
         SENTADO USANDO PC       0.02      0.01      0.01       184
      SENTADO VIENDO LA TV       0.21      0.15      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.02      0.01      0.01       184
                    TROTAR       0.48      0.57      0.52       161

                  accuracy                           0.28      2737
                 macro avg       0.22      0.28      0.23      2737
              weighted avg       0.22      0.28      0.23      2737

2025-11-05 17:41:53.261680: 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 17:41:53.273070: 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 00:00:1762360913.286525 4071836 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762360913.290672 4071836 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762360913.300497 4071836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360913.300512 4071836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360913.300514 4071836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762360913.300515 4071836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:41:53.303657: 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 00:00:1762360915.568206 4071836 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762360917.766246 4071947 service.cc:152] XLA service 0x72c0f8021ce0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762360917.766275 4071947 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:41:57.809122: 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 00:00:1762360918.039232 4071947 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762360920.481596 4071947 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1053 - loss: 2.7135 - val_accuracy: 0.2098 - val_loss: 2.4521
Epoch 8/132

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Epoch 9/132

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Epoch 10/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1352 - loss: 2.6272  
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Epoch 11/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4067
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1356 - loss: 2.6125  
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Epoch 12/132

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Epoch 13/132

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Epoch 14/132

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Epoch 15/132

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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1835 - loss: 2.4682
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1835 - loss: 2.4680 - val_accuracy: 0.2303 - val_loss: 2.3593
Epoch 21/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4361
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1750 - loss: 2.4859  
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[1m509/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1839 - loss: 2.4668
[1m547/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1840 - loss: 2.4665
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1840 - loss: 2.4661 - val_accuracy: 0.2452 - val_loss: 2.3502
Epoch 22/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1250 - loss: 2.5027
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1883 - loss: 2.4826  
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Epoch 23/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1902 - loss: 2.4443 - val_accuracy: 0.2452 - val_loss: 2.3298
Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4371
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[1m549/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1911 - loss: 2.4360
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Epoch 25/132

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Epoch 26/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 24ms/step - accuracy: 0.1875 - loss: 2.3756
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[1m522/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.4290
[1m556/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1935 - loss: 2.4288
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1935 - loss: 2.4286 - val_accuracy: 0.2728 - val_loss: 2.3160
Epoch 27/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0625 - loss: 2.5904
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1603 - loss: 2.4576  
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Epoch 28/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1924 - loss: 2.4158 - val_accuracy: 0.2797 - val_loss: 2.3089
Epoch 29/132

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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1999 - loss: 2.4104
[1m576/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1998 - loss: 2.4104
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1998 - loss: 2.4104 - val_accuracy: 0.2787 - val_loss: 2.3058
Epoch 30/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2047 - loss: 2.3799  
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Epoch 31/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1993 - loss: 2.4016 - val_accuracy: 0.2793 - val_loss: 2.2994
Epoch 32/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2087 - loss: 2.3635  
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[1m319/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.3838
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2074 - loss: 2.3906 - val_accuracy: 0.2682 - val_loss: 2.2985
Epoch 33/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1962 - loss: 2.3829  
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Epoch 34/132

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Epoch 35/132

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[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1659 - loss: 2.4590  
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Epoch 36/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2065 - loss: 2.3799 - val_accuracy: 0.2787 - val_loss: 2.2800
Epoch 37/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2114 - loss: 2.3915  
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[1m320/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.3835
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[1m533/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2118 - loss: 2.3817
[1m569/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2119 - loss: 2.3814
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2120 - loss: 2.3813 - val_accuracy: 0.2905 - val_loss: 2.2769
Epoch 38/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2902
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1896 - loss: 2.3612  
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Epoch 39/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2182 - loss: 2.3663 - val_accuracy: 0.3022 - val_loss: 2.2731
Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2172 - loss: 2.3591 - val_accuracy: 0.3034 - val_loss: 2.2580
Epoch 45/132

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Epoch 46/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.1562 - loss: 2.2698
[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2207 - loss: 2.3680  
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Epoch 47/132

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Epoch 48/132

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Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2335 - loss: 2.3113  
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[1m289/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.3352
[1m323/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2213 - loss: 2.3347
[1m359/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2218 - loss: 2.3343
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[1m571/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.3337
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Epoch 52/132

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Epoch 53/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5757
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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2213 - loss: 2.3345
[1m546/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2219 - loss: 2.3337
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2224 - loss: 2.3333 - val_accuracy: 0.3041 - val_loss: 2.2375
Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.3125 - loss: 2.2906
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2422 - loss: 2.3690  
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Epoch 55/132

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Epoch 56/132

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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2260 - loss: 2.3219
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Epoch 57/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1400
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Epoch 58/132

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Epoch 59/132

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Epoch 60/132

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Epoch 61/132

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Epoch 62/132

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Epoch 63/132

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Epoch 64/132

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Epoch 65/132

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Epoch 66/132

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Epoch 67/132

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Epoch 68/132

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Epoch 69/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2354 - loss: 2.2911 - val_accuracy: 0.3198 - val_loss: 2.1937
Epoch 70/132

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Epoch 71/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1551
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[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2465 - loss: 2.3072
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Epoch 72/132

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Epoch 73/132

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Epoch 74/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2443 - loss: 2.2653 - val_accuracy: 0.3276 - val_loss: 2.1855
Epoch 75/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2403 - loss: 2.2768 - val_accuracy: 0.3333 - val_loss: 2.1835
Epoch 76/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0938 - loss: 2.4916
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2363 - loss: 2.2525  
[1m 75/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2423 - loss: 2.2536
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[1m506/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.2686
[1m543/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.2691
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2430 - loss: 2.2694 - val_accuracy: 0.3286 - val_loss: 2.1830
Epoch 77/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.2188 - loss: 2.4798
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2128 - loss: 2.3496  
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[1m142/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2294 - loss: 2.3105
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[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.2817
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2407 - loss: 2.2812
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2407 - loss: 2.2812 - val_accuracy: 0.3333 - val_loss: 2.1871
Epoch 78/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.2188 - loss: 2.4296
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2151 - loss: 2.3021  
[1m 73/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2344 - loss: 2.2595
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2444 - loss: 2.2574 - val_accuracy: 0.3341 - val_loss: 2.1850

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36s[0m 426ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 813us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 26: 27.62 [%]
F1-score capturado en la ejecución 26: 22.9 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m 51/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m120/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 844us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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Global accuracy score (validation) = 33.41 [%]
Global F1 score (validation) = 29.5 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00464978 0.00720212 0.00332664 ... 0.08487453 0.005318   0.00104912]
 [0.0029492  0.00439841 0.00220652 ... 0.09911549 0.00354073 0.00070637]
 [0.00184524 0.00292468 0.00132309 ... 0.08008928 0.0022242  0.00038708]
 ...
 [0.15640366 0.06362249 0.1459416  ... 0.01747099 0.16808447 0.09231773]
 [0.13712795 0.08529134 0.13294297 ... 0.02483697 0.13505924 0.08034717]
 [0.18151104 0.04498638 0.16918209 ... 0.0044444  0.20651463 0.12639387]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 30.83 [%]
Global accuracy score (test) = 28.79 [%]
Global F1 score (train) = 27.66 [%]
Global F1 score (test) = 25.46 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.36      0.30       184
       CAMINAR USUAL SPEED       0.12      0.04      0.06       184
            CAMINAR ZIGZAG       0.20      0.44      0.28       184
          DE PIE BARRIENDO       0.15      0.03      0.05       184
   DE PIE DOBLANDO TOALLAS       0.25      0.47      0.32       184
    DE PIE MOVIENDO LIBROS       0.27      0.25      0.26       184
          DE PIE USANDO PC       0.13      0.03      0.05       184
        FASE REPOSO CON K5       0.34      0.76      0.47       184
INCREMENTAL CICLOERGOMETRO       0.42      0.46      0.44       184
           SENTADO LEYENDO       0.34      0.32      0.33       184
         SENTADO USANDO PC       0.10      0.03      0.04       184
      SENTADO VIENDO LA TV       0.30      0.34      0.32       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.28      0.25       184
                    TROTAR       0.81      0.53      0.64       161

                  accuracy                           0.29      2737
                 macro avg       0.26      0.29      0.25      2737
              weighted avg       0.26      0.29      0.25      2737

2025-11-05 17:43:38.753472: 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 17:43:38.764728: 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 00:00:1762361018.777717 4080635 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762361018.781679 4080635 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762361018.791891 4080635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361018.791908 4080635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361018.791917 4080635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361018.791919 4080635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:43:38.795167: 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 00:00:1762361021.059120 4080635 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762361023.204626 4080745 service.cc:152] XLA service 0x7f2efc005f50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762361023.204669 4080745 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:43:43.253084: 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 00:00:1762361023.480542 4080745 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762361025.934144 4080745 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40:23[0m 4s/step - accuracy: 0.1562 - loss: 4.1831
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Epoch 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1562 - loss: 2.6879
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Epoch 8/132

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Saved model to disk.

Accuracy capturado en la ejecución 27: 28.79 [%]
F1-score capturado en la ejecución 27: 25.46 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m143/158[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 710us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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Global accuracy score (validation) = 21.24 [%]
Global F1 score (validation) = 10.09 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.02846747 0.06955599 0.03494005 ... 0.06877316 0.03299814 0.02622996]
 [0.02855327 0.06944294 0.03450381 ... 0.07037669 0.03287756 0.02644868]
 [0.03187037 0.07080264 0.03536091 ... 0.0695181  0.03523823 0.02704908]
 ...
 [0.10845835 0.05988931 0.10999778 ... 0.04840853 0.11095757 0.10340928]
 [0.10782345 0.0599191  0.10894255 ... 0.0489685  0.10975386 0.10331338]
 [0.10950934 0.06010498 0.11165818 ... 0.04788664 0.11260941 0.10283382]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 17.62 [%]
Global accuracy score (test) = 17.5 [%]
Global F1 score (train) = 8.18 [%]
Global F1 score (test) = 8.35 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.13      0.68      0.22       184
          DE PIE BARRIENDO       0.32      0.03      0.06       184
   DE PIE DOBLANDO TOALLAS       0.25      0.02      0.03       184
    DE PIE MOVIENDO LIBROS       0.02      0.01      0.01       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.18      0.81      0.30       184
INCREMENTAL CICLOERGOMETRO       0.26      0.70      0.38       184
           SENTADO LEYENDO       0.17      0.35      0.23       184
         SENTADO USANDO PC       1.00      0.01      0.02       184
      SENTADO VIENDO LA TV       0.00      0.00      0.00       184
   SUBIR Y BAJAR ESCALERAS       0.00      0.00      0.00       184
                    TROTAR       0.00      0.00      0.00       161

                  accuracy                           0.18      2737
                 macro avg       0.16      0.17      0.08      2737
              weighted avg       0.16      0.18      0.08      2737

2025-11-05 17:44:09.994117: 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 17:44:10.005668: 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 00:00:1762361050.019385 4082419 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762361050.023584 4082419 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762361050.033339 4082419 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361050.033356 4082419 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361050.033357 4082419 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361050.033358 4082419 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:44:10.036448: 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 00:00:1762361052.266251 4082419 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762361054.398679 4082529 service.cc:152] XLA service 0x7a3b80003550 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762361054.398722 4082529 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:44:14.448059: 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 00:00:1762361054.672900 4082529 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762361057.088746 4082529 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 2/132

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Epoch 3/132

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Epoch 4/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0973 - loss: 3.0290  
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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.0996 - loss: 2.7305 - val_accuracy: 0.1971 - val_loss: 2.5049
Epoch 8/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1132 - loss: 2.6836 - val_accuracy: 0.1979 - val_loss: 2.4956
Epoch 9/132

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[1m459/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1169 - loss: 2.6584
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[1m525/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1172 - loss: 2.6578
[1m562/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1175 - loss: 2.6574
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1176 - loss: 2.6572 - val_accuracy: 0.2128 - val_loss: 2.4794
Epoch 10/132

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[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1425 - loss: 2.6542
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Epoch 11/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1331 - loss: 2.6212 - val_accuracy: 0.2124 - val_loss: 2.4469
Epoch 12/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4792
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1611 - loss: 2.5742  
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Epoch 13/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1389 - loss: 2.5937  
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Epoch 14/132

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Epoch 15/132

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[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1611 - loss: 2.5375  
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Epoch 16/132

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Epoch 17/132

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Epoch 18/132

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Epoch 19/132

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Epoch 20/132

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[1m529/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1824 - loss: 2.4639
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1821 - loss: 2.4649 - val_accuracy: 0.2299 - val_loss: 2.3467
Epoch 21/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1819 - loss: 2.4583  
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Epoch 22/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1828 - loss: 2.4588 - val_accuracy: 0.2450 - val_loss: 2.3329
Epoch 23/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4128
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1738 - loss: 2.4460  
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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1855 - loss: 2.4514
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1858 - loss: 2.4512
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Epoch 24/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5858
[1m 35/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2065 - loss: 2.4371  
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Epoch 25/132

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Epoch 26/132

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Epoch 27/132

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Epoch 28/132

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Epoch 29/132

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2008 - loss: 2.3983 - val_accuracy: 0.2577 - val_loss: 2.2997
Epoch 30/132

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[1m 29/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1855 - loss: 2.4010  
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[1m510/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1965 - loss: 2.4077
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1970 - loss: 2.4074 - val_accuracy: 0.2573 - val_loss: 2.2860
Epoch 31/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.2500 - loss: 2.2738
[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2097 - loss: 2.3407  
[1m 71/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2083 - loss: 2.3622
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[1m219/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2031 - loss: 2.3871
[1m254/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2025 - loss: 2.3896
[1m291/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2020 - loss: 2.3913
[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2016 - loss: 2.3927
[1m359/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.3939
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[1m434/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2013 - loss: 2.3954
[1m469/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2013 - loss: 2.3958
[1m506/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.3961
[1m539/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.3964
[1m575/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.3967
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2015 - loss: 2.3967 - val_accuracy: 0.2621 - val_loss: 2.2845
Epoch 32/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.2188 - loss: 2.3497
[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1979 - loss: 2.4022  
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1962 - loss: 2.4051
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[1m180/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1953 - loss: 2.4055
[1m214/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1963 - loss: 2.4037
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Epoch 33/132

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Epoch 34/132

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[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.3667  
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Epoch 35/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5446
[1m 33/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1957 - loss: 2.4191  
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Epoch 36/132

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[1m 36/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2237 - loss: 2.3988  
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Epoch 37/132

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[1m 31/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2080 - loss: 2.3960  
[1m 65/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2071 - loss: 2.3899
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[1m209/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2086 - loss: 2.3831
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[1m279/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2095 - loss: 2.3803
[1m310/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2097 - loss: 2.3796
[1m346/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2098 - loss: 2.3793
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[1m523/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2094 - loss: 2.3790
[1m558/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2093 - loss: 2.3790
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2092 - loss: 2.3789 - val_accuracy: 0.2795 - val_loss: 2.2658
Epoch 38/132

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Epoch 39/132

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Epoch 40/132

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Epoch 41/132

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Epoch 42/132

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Epoch 43/132

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Epoch 44/132

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Epoch 45/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4315
[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2034 - loss: 2.3823  
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[1m291/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2148 - loss: 2.3560
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[1m363/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2148 - loss: 2.3544
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[1m548/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2156 - loss: 2.3526
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Epoch 46/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 23ms/step - accuracy: 0.0938 - loss: 2.3480
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2167 - loss: 2.3113  
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Epoch 47/132

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Epoch 48/132

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[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2331 - loss: 2.3462  
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[1m324/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2296 - loss: 2.3310
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2267 - loss: 2.3301 - val_accuracy: 0.3202 - val_loss: 2.2240
Epoch 49/132

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Epoch 50/132

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Epoch 51/132

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[1m 32/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2431 - loss: 2.2865  
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Epoch 52/132

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Epoch 53/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4938
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[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2311 - loss: 2.3146
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2309 - loss: 2.3149
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2308 - loss: 2.3150 - val_accuracy: 0.3097 - val_loss: 2.2123
Epoch 54/132

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1639
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Epoch 55/132

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Epoch 56/132

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[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2326 - loss: 2.3024
[1m576/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2324 - loss: 2.3029
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Epoch 57/132

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Epoch 58/132

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Epoch 59/132

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[1m 38/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2041 - loss: 2.2887  
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2095 - loss: 2.2946
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2256 - loss: 2.2992 - val_accuracy: 0.3194 - val_loss: 2.1975
Epoch 60/132

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Epoch 61/132

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Saved model to disk.

Accuracy capturado en la ejecución 28: 17.5 [%]
F1-score capturado en la ejecución 28: 8.35 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

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[1m127/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 797us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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Global accuracy score (validation) = 32.4 [%]
Global F1 score (validation) = 27.35 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0068652  0.00890271 0.00457059 ... 0.09645592 0.0067587  0.00197915]
 [0.00770757 0.00956417 0.00535742 ... 0.10206737 0.00757495 0.00242243]
 [0.00336428 0.00354673 0.00204979 ... 0.06695558 0.00341823 0.00082551]
 ...
 [0.14346777 0.06519947 0.13679755 ... 0.02066663 0.15147187 0.09222041]
 [0.13864674 0.06703261 0.13171028 ... 0.02389808 0.14505374 0.08674515]
 [0.15938397 0.05222113 0.15159559 ... 0.01144155 0.17623408 0.11517683]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 29.24 [%]
Global accuracy score (test) = 28.28 [%]
Global F1 score (train) = 24.79 [%]
Global F1 score (test) = 23.59 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.33      0.28       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.19      0.63      0.30       184
          DE PIE BARRIENDO       0.17      0.03      0.05       184
   DE PIE DOBLANDO TOALLAS       0.23      0.48      0.31       184
    DE PIE MOVIENDO LIBROS       0.30      0.27      0.29       184
          DE PIE USANDO PC       0.00      0.00      0.00       184
        FASE REPOSO CON K5       0.32      0.76      0.45       184
INCREMENTAL CICLOERGOMETRO       0.39      0.49      0.44       184
           SENTADO LEYENDO       0.35      0.38      0.37       184
         SENTADO USANDO PC       0.00      0.00      0.00       184
      SENTADO VIENDO LA TV       0.37      0.33      0.35       184
   SUBIR Y BAJAR ESCALERAS       0.24      0.05      0.08       184
                    TROTAR       0.77      0.52      0.62       161

                  accuracy                           0.28      2737
                 macro avg       0.24      0.28      0.24      2737
              weighted avg       0.24      0.28      0.23      2737

2025-11-05 17:45:36.916565: 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 17:45:36.927808: 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 00:00:1762361136.941003 4089516 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762361136.945099 4089516 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762361136.954775 4089516 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361136.954793 4089516 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361136.954794 4089516 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762361136.954796 4089516 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 17:45:36.957910: 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 00:00:1762361139.214242 4089516 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/132
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762361141.370308 4089644 service.cc:152] XLA service 0x7f68fc002550 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762361141.370333 4089644 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 17:45:41.414506: 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 00:00:1762361141.649900 4089644 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762361144.096243 4089644 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 2/132

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Epoch 3/132

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Epoch 4/132

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Epoch 5/132

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Epoch 6/132

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Epoch 7/132

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[1m541/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1093 - loss: 2.7062
[1m575/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1093 - loss: 2.7056
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1093 - loss: 2.7055 - val_accuracy: 0.1781 - val_loss: 2.4879

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 416ms/step
[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 785us/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 28.28 [%]
F1-score capturado en la ejecución 29: 23.59 [%]

=== 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 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 3, 64)          │        12,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 3, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 3, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 74,127 (289.56 KB)
 Trainable params: 74,127 (289.56 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 3, 250)
(18469, 3, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:09[0m 952ms/step
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[1m132/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 768us/step
[1m201/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 755us/step
[1m269/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 752us/step
[1m338/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 747us/step
[1m408/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 743us/step
[1m477/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 741us/step
[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 745us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 934us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 66/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 778us/step
[1m137/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 742us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 17.81 [%]
Global F1 score (validation) = 9.08 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.02714434 0.05321878 0.02680665 ... 0.06760106 0.02629216 0.02021723]
 [0.02423463 0.05049013 0.02685743 ... 0.068036   0.0264178  0.01947715]
 [0.03162774 0.05811161 0.02959479 ... 0.06933681 0.02793092 0.02222807]
 ...
 [0.11085384 0.05412626 0.09544683 ... 0.05690555 0.10677673 0.08491142]
 [0.11025582 0.05322055 0.09457216 ... 0.05837524 0.10482535 0.0842745 ]
 [0.11065602 0.05434309 0.09589223 ... 0.05723971 0.10689119 0.08413616]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 17.3 [%]
Global accuracy score (test) = 16.88 [%]
Global F1 score (train) = 9.23 [%]
Global F1 score (test) = 9.12 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.16      0.61      0.25       184
 CAMINAR CON MÓVIL O LIBRO       0.00      0.00      0.00       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.14      0.14      0.14       184
          DE PIE BARRIENDO       0.10      0.01      0.01       184
   DE PIE DOBLANDO TOALLAS       0.38      0.03      0.06       184
    DE PIE MOVIENDO LIBROS       0.08      0.18      0.11       184
          DE PIE USANDO PC       0.25      0.01      0.02       184
        FASE REPOSO CON K5       0.18      0.78      0.29       184
INCREMENTAL CICLOERGOMETRO       0.26      0.70      0.38       184
           SENTADO LEYENDO       0.20      0.01      0.01       184
         SENTADO USANDO PC       0.40      0.02      0.04       184
      SENTADO VIENDO LA TV       0.00      0.00      0.00       184
   SUBIR Y BAJAR ESCALERAS       0.03      0.01      0.01       184
                    TROTAR       0.36      0.02      0.05       161

                  accuracy                           0.17      2737
                 macro avg       0.17      0.17      0.09      2737
              weighted avg       0.17      0.17      0.09      2737


Accuracy capturado en la ejecución 30: 16.88 [%]
F1-score capturado en la ejecución 30: 9.12 [%]

=== RESUMEN FINAL ===
Accuracies: [17.5, 29.63, 16.62, 28.79, 29.41, 29.81, 29.01, 27.51, 29.52, 17.35, 27.0, 28.5, 29.74, 27.99, 28.21, 16.88, 27.69, 28.17, 29.16, 28.97, 30.65, 17.61, 28.9, 27.99, 27.88, 27.62, 28.79, 17.5, 28.28, 16.88]
F1-scores: [9.99, 26.04, 7.88, 24.77, 26.47, 26.21, 25.86, 24.53, 26.86, 8.78, 22.38, 26.17, 24.55, 23.93, 24.29, 9.15, 25.22, 25.39, 25.67, 25.6, 28.13, 9.08, 24.79, 26.16, 23.08, 22.9, 25.46, 8.35, 23.59, 9.12]
Accuracy mean: 25.9853 | std: 4.9141
F1 mean: 21.3467 | std: 6.9687

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_ESANN_acc_17_classes/metrics_test.npz
