2025-10-27 11:52:01.946254: 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`.
2025-10-27 11:52:01.958808: 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 00:00:1761562321.972660  569414 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761562321.980368  569414 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761562321.995762  569414 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761562321.995792  569414 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761562321.995795  569414 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761562321.995797  569414 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 11:52:01.999912: 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(
2025-10-27 11:52:05,359	INFO worker.py:1927 -- Started a local Ray instance.
2025-10-27 11:52:06,069	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-10-27 11:52:06,135	INFO trial.py:182 -- Creating a new dirname dir_fa758_3dcb because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,138	INFO trial.py:182 -- Creating a new dirname dir_fa758_6ce1 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,140	INFO trial.py:182 -- Creating a new dirname dir_fa758_8882 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,142	INFO trial.py:182 -- Creating a new dirname dir_fa758_4cc9 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,145	INFO trial.py:182 -- Creating a new dirname dir_fa758_6ab4 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,147	INFO trial.py:182 -- Creating a new dirname dir_fa758_5421 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,150	INFO trial.py:182 -- Creating a new dirname dir_fa758_50b3 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,152	INFO trial.py:182 -- Creating a new dirname dir_fa758_d019 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,154	INFO trial.py:182 -- Creating a new dirname dir_fa758_5c20 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,157	INFO trial.py:182 -- Creating a new dirname dir_fa758_a658 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,161	INFO trial.py:182 -- Creating a new dirname dir_fa758_f7da because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,163	INFO trial.py:182 -- Creating a new dirname dir_fa758_c5c2 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,166	INFO trial.py:182 -- Creating a new dirname dir_fa758_755a because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,169	INFO trial.py:182 -- Creating a new dirname dir_fa758_2152 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,172	INFO trial.py:182 -- Creating a new dirname dir_fa758_9a51 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,176	INFO trial.py:182 -- Creating a new dirname dir_fa758_732b because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,182	INFO trial.py:182 -- Creating a new dirname dir_fa758_b1ea because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,189	INFO trial.py:182 -- Creating a new dirname dir_fa758_a1e5 because trial dirname 'dir_fa758' already exists.
2025-10-27 11:52:06,194	INFO trial.py:182 -- Creating a new dirname dir_fa758_84da because trial dirname 'dir_fa758' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     ESANN_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator          │
│ Scheduler                        AsyncHyperBandScheduler        │
│ Number of trials                 20                             │
╰─────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_ESANN_acc_gyr_17_classes/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-10-27_11-52-04_512671_569414/artifacts/2025-10-27_11-52-06/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-10-27 11:52:06. 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_fa758    PENDING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    PENDING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    PENDING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    PENDING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    PENDING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    PENDING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    PENDING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    PENDING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    PENDING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    PENDING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    PENDING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    PENDING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    PENDING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    PENDING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    PENDING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    PENDING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    PENDING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    PENDING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    PENDING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    PENDING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            42 │
│ funcion_activacion              tanh │
│ numero_filtros                   256 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00039 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            8 │
│ epochs                            97 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00212 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_fa758 config            │
├─────────────────────────────────────┤
│ N_capas                           8 │
│ epochs                           64 │
│ funcion_activacion             tanh │
│ numero_filtros                  256 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                 64 │
│ tasa_aprendizaje             0.0006 │
╰─────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            6 │
│ epochs                            37 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            5 │
│ epochs                            49 │
│ funcion_activacion              tanh │
│ numero_filtros                   256 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00102 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            82 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            8 │
│ epochs                            65 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            6 │
│ epochs                            61 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00735 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            92 │
│ funcion_activacion              relu │
│ numero_filtros                   256 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00024 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            7 │
│ epochs                            67 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00903 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            5 │
│ epochs                            31 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
[36m(train_cnn_ray_tune pid=571043)[0m 2025-10-27 11:52:09.396967: 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=571043)[0m 2025-10-27 11:52:09.417638: 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=571043)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=571043)[0m E0000 00:00:1761562329.444534  572264 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=571043)[0m E0000 00:00:1761562329.453487  572264 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=571077)[0m W0000 00:00:1761562329.471586  572263 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=571077)[0m W0000 00:00:1761562329.471629  572263 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=571077)[0m W0000 00:00:1761562329.471632  572263 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=571077)[0m W0000 00:00:1761562329.471634  572263 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=571077)[0m 2025-10-27 11:52:09.477452: 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=571077)[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=571043)[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=571043)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=571043)[0m 2025-10-27 11:52:13.142854: 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=571043)[0m 2025-10-27 11:52:13.142903: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=571043)[0m 2025-10-27 11:52:13.142911: 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=571043)[0m 2025-10-27 11:52:13.142917: 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=571043)[0m 2025-10-27 11:52:13.142922: 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=571043)[0m 2025-10-27 11:52:13.142925: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=571043)[0m 2025-10-27 11:52:13.143187: 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=571043)[0m 2025-10-27 11:52:13.143227: 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=571043)[0m 2025-10-27 11:52:13.143233: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            7 │
│ epochs                            70 │
│ funcion_activacion              tanh │
│ numero_filtros                   256 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00312 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            6 │
│ epochs                            76 │
│ funcion_activacion              relu │
│ numero_filtros                   256 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            69 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            6 │
│ epochs                            82 │
│ funcion_activacion              relu │
│ numero_filtros                   256 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            5 │
│ epochs                            45 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00022 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            38 │
│ funcion_activacion              relu │
│ numero_filtros                   256 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            6 │
│ epochs                            75 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00011 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_fa758 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            39 │
│ funcion_activacion              tanh │
│ numero_filtros                   256 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_fa758 started with configuration:
╭────────────────────────────────────╮
│ Trial trial_fa758 config           │
├────────────────────────────────────┤
│ N_capas                          6 │
│ epochs                          50 │
│ funcion_activacion            relu │
│ numero_filtros                 128 │
│ optimizador                   adam │
│ tamanho_filtro                   3 │
│ tamanho_minilote                64 │
│ tasa_aprendizaje             0.005 │
╰────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=571080)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=571080)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=571080)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=571080)[0m │ conv1d (Conv1D)                 │ (None, 6, 32)          │        40,032 │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ layer_normalization             │ (None, 6, 32)          │            64 │
[36m(train_cnn_ray_tune pid=571080)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ dropout (Dropout)               │ (None, 6, 32)          │             0 │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ conv1d_1 (Conv1D)               │ (None, 6, 32)          │         5,152 │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ layer_normalization_1           │ (None, 6, 32)          │            64 │
[36m(train_cnn_ray_tune pid=571080)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ dropout_1 (Dropout)             │ (None, 6, 32)          │             0 │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ conv1d_2 (Conv1D)               │ (None, 6, 32)          │         5,152 │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ layer_normalization_2           │ (None, 6, 32)          │            64 │
[36m(train_cnn_ray_tune pid=571080)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ dropout_2 (Dropout)             │ (None, 6, 32)          │             0 │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=571080)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ dropout_3 (Dropout)             │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=571080)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=571080)[0m │ dense (Dense)                   │ (None, 15)             │           495 │
[36m(train_cnn_ray_tune pid=571080)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=571080)[0m  Total params: 51,023 (199.31 KB)
[36m(train_cnn_ray_tune pid=571080)[0m  Trainable params: 51,023 (199.31 KB)
[36m(train_cnn_ray_tune pid=571080)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=571055)[0m  Total params: 1,309,967 (5.00 MB)
[36m(train_cnn_ray_tune pid=571055)[0m  Trainable params: 1,309,967 (5.00 MB)
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 1/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30:48[0m 3s/step - accuracy: 0.0000e+00 - loss: 3.1453
[1m  4/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.0339 - loss: 3.1389    
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  8/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 18ms/step - accuracy: 0.0413 - loss: 3.1330 
[1m 12/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.0439 - loss: 3.1357
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 16/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.0461 - loss: 3.1301
[1m 19/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.0480 - loss: 3.1246
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 23/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.0505 - loss: 3.1190
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 27/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.0522 - loss: 3.1136
[1m 31/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.0534 - loss: 3.1086
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 36/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 16ms/step - accuracy: 0.0542 - loss: 3.1028
[1m 39/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 16ms/step - accuracy: 0.0548 - loss: 3.0993
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:11[0m 3s/step - accuracy: 0.0312 - loss: 3.2917
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 30ms/step - accuracy: 0.0521 - loss: 3.1533
[1m  5/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 35ms/step - accuracy: 0.0631 - loss: 3.1221
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  7/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 36ms/step - accuracy: 0.0706 - loss: 3.1023
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 63/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 16ms/step - accuracy: 0.0589 - loss: 3.0799
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  8/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 39ms/step - accuracy: 0.0720 - loss: 3.0984
[1m 10/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 39ms/step - accuracy: 0.0760 - loss: 3.0864
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 12/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 41ms/step - accuracy: 0.0779 - loss: 3.0843
[36m(train_cnn_ray_tune pid=571085)[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=571085)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m │ global_average_pooling1d        │ (None, 256)            │             0 │[32m [repeated 214x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 359x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m │ layer_normalization             │ (None, 6, 256)         │           512 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 107x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m │ dropout (Dropout)               │ (None, 6, 256)         │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m │ dropout_7 (Dropout)             │ (None, 256)            │             0 │[32m [repeated 107x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m │ dense (Dense)                   │ (None, 15)             │         3,855 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m  Total params: 37,263 (145.56 KB)[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m  Trainable params: 37,263 (145.56 KB)[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m  Total params: 2,295,311 (8.76 MB)[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m  Trainable params: 2,295,311 (8.76 MB)[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 14/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 41ms/step - accuracy: 0.0782 - loss: 3.0845
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 16/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 40ms/step - accuracy: 0.0780 - loss: 3.0874
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 18/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 41ms/step - accuracy: 0.0780 - loss: 3.0905
[1m 20/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 41ms/step - accuracy: 0.0785 - loss: 3.0929
[36m(train_cnn_ray_tune pid=571082)[0m Epoch 1/75[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 22/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 41ms/step - accuracy: 0.0791 - loss: 3.0945
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 23/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 42ms/step - accuracy: 0.0793 - loss: 3.0956
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 25/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 42ms/step - accuracy: 0.0796 - loss: 3.0966
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 26/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 43ms/step - accuracy: 0.0796 - loss: 3.0974
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-27 11:52:36. 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_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 2/37
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 2/45
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-27 11:53:06. 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_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 2/61[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m 92/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1:33[0m 511ms/step - accuracy: 0.1023 - loss: 2.9649[32m [repeated 38x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 2/31
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 542ms/step - accuracy: 0.0938 - loss: 2.9660
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 3/37
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m Epoch 2/65[32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 3/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:19[0m 146ms/step - accuracy: 0.1562 - loss: 3.0582
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 66/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 64ms/step - accuracy: 0.1138 - loss: 2.7575[32m [repeated 138x across cluster][0m
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:31[0m 334ms/step - accuracy: 0.2812 - loss: 2.2752
[36m(train_cnn_ray_tune pid=571083)[0m Epoch 2/50
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 3/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-27 11:53:36. 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_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m141/274[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1:08[0m 512ms/step - accuracy: 0.1066 - loss: 2.8851[32m [repeated 39x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 3/45
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 2/39
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 4/37[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m Epoch 3/65[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-27 11:54:06. 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_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 2/97
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 4/82
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 82/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1:05[0m 141ms/step - accuracy: 0.1183 - loss: 2.8136[32m [repeated 96x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m Epoch 2/49[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 3/92[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m Epoch 4/67[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m Epoch 3/50
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-27 11:54:36. Total running time: 2min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 6/37
[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[1m174/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m12s[0m 123ms/step - accuracy: 0.1297 - loss: 2.3774[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m Epoch 4/65
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 278ms/step - accuracy: 0.0312 - loss: 3.1286
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 5/45
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m39s[0m 71ms/step - accuracy: 0.1339 - loss: 2.6411 - val_accuracy: 0.2014 - val_loss: 2.1992
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m169/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1:09[0m 183ms/step - accuracy: 0.1411 - loss: 2.7569[32m [repeated 33x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 47/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m27s[0m 122ms/step - accuracy: 0.0598 - loss: 3.0467
[1m 48/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m27s[0m 121ms/step - accuracy: 0.0598 - loss: 3.0461[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 43/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 54ms/step - accuracy: 0.1563 - loss: 2.5702[32m [repeated 121x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 5/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:46[0m 195ms/step - accuracy: 0.1250 - loss: 2.8072
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m542/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 61ms/step - accuracy: 0.1005 - loss: 2.8522
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m546/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 61ms/step - accuracy: 0.1005 - loss: 2.8522
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 61ms/step - accuracy: 0.1005 - loss: 2.8521
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 75/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 55ms/step - accuracy: 0.1539 - loss: 2.5716
[1m 76/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 55ms/step - accuracy: 0.1538 - loss: 2.5715[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 5/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 2/76[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:53[0m 428ms/step - accuracy: 0.0625 - loss: 2.7656[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[1m151/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m24s[0m 61ms/step - accuracy: 0.1010 - loss: 2.8310[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 21/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 120ms/step - accuracy: 0.1255 - loss: 2.3392[32m [repeated 298x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m Epoch 5/67
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 266ms/step - accuracy: 0.1250 - loss: 2.3372
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m37s[0m 136ms/step - accuracy: 0.1300 - loss: 2.3720 - val_accuracy: 0.1346 - val_loss: 2.1903
[36m(train_cnn_ray_tune pid=571069)[0m 
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[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m14s[0m 126ms/step - accuracy: 0.0635 - loss: 3.0214[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 180ms/step - accuracy: 0.0677 - loss: 3.0469 - val_accuracy: 0.0872 - val_loss: 2.6915
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 7/37
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 290ms/step - accuracy: 0.0547 - loss: 3.1669
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m234/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m11s[0m 286ms/step - accuracy: 0.1973 - loss: 2.4430[32m [repeated 298x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-10-27 11:55:06. Total running time: 3min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 50/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:55[0m 232ms/step - accuracy: 0.1130 - loss: 2.7933[32m [repeated 58x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m474/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m9s[0m 137ms/step - accuracy: 0.1822 - loss: 2.2614 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 3/39
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 4/92
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 6/45
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m Epoch 4/50[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 3/97[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m Epoch 3/75[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-27 11:55:36. Total running time: 3min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m Epoch 6/67
[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m Epoch 3/49
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 7/45
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 9/37
[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 7/61[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-27 11:56:06. Total running time: 4min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:00[0m 221ms/step - accuracy: 0.1250 - loss: 2.9486
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 50/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 322ms/step - accuracy: 0.2988 - loss: 1.8700[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m56s[0m 404ms/step - accuracy: 0.2623 - loss: 2.0820 - val_accuracy: 0.3484 - val_loss: 1.6275[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 7/69[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:46[0m 196ms/step - accuracy: 0.0938 - loss: 2.6046[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m153/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m12s[0m 105ms/step - accuracy: 0.1340 - loss: 2.4972
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m23s[0m 124ms/step - accuracy: 0.0734 - loss: 2.9837[32m [repeated 264x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m 82/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 61ms/step - accuracy: 0.1070 - loss: 2.7638
[1m 83/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m28s[0m 61ms/step - accuracy: 0.1070 - loss: 2.7636[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1:39[0m 532ms/step - accuracy: 0.1180 - loss: 2.5974[32m [repeated 47x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m112s[0m 204ms/step - accuracy: 0.1465 - loss: 2.7251 - val_accuracy: 0.2291 - val_loss: 2.1969
[36m(train_cnn_ray_tune pid=571049)[0m Epoch 3/38
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:02[0m 334ms/step - accuracy: 0.0312 - loss: 2.6408
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m37s[0m 136ms/step - accuracy: 0.1356 - loss: 2.3231 - val_accuracy: 0.1492 - val_loss: 2.1838
[36m(train_cnn_ray_tune pid=571076)[0m Epoch 7/67
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:24[0m 308ms/step - accuracy: 0.0469 - loss: 2.3911
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m321/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m32s[0m 142ms/step - accuracy: 0.2357 - loss: 2.0124
[1m322/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m31s[0m 142ms/step - accuracy: 0.2357 - loss: 2.0123[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m126/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m18s[0m 125ms/step - accuracy: 0.0714 - loss: 2.9911[32m [repeated 242x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m158/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m24s[0m 62ms/step - accuracy: 0.1080 - loss: 2.7623
[1m160/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m24s[0m 62ms/step - accuracy: 0.1081 - loss: 2.7623[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 81/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1:02[0m 325ms/step - accuracy: 0.2980 - loss: 1.8689[32m [repeated 55x across cluster][0m
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m57s[0m 207ms/step - accuracy: 0.3706 - loss: 1.6289 - val_accuracy: 0.4081 - val_loss: 1.4221
[36m(train_cnn_ray_tune pid=571083)[0m Epoch 5/50
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 10/37
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 4/39[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[1m 77/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m20s[0m 106ms/step - accuracy: 0.1356 - loss: 2.4645[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m80s[0m 580ms/step - accuracy: 0.0728 - loss: 3.0963 - val_accuracy: 0.1466 - val_loss: 2.4273
[36m(train_cnn_ray_tune pid=571079)[0m Epoch 4/82
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:29[0m 656ms/step - accuracy: 0.1172 - loss: 3.0696
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-27 11:56:36. Total running time: 4min 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_fa758    RUNNING            8   adam            tanh                                   64                256                  5          0.000595066         64 │
│ trial_fa758    RUNNING            4   rmsprop         relu                                   32                256                  5          4.07199e-05         38 │
│ trial_fa758    RUNNING            8   rmsprop         relu                                   32                 64                  5          0.00211867          97 │
│ trial_fa758    RUNNING            6   adam            relu                                  128                256                  3          1.69169e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                 32                  3          0.00903085          67 │
│ trial_fa758    RUNNING            3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69 │
│ trial_fa758    RUNNING            3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82 │
│ trial_fa758    RUNNING            7   rmsprop         tanh                                   64                256                  5          0.00312316          70 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                   64                256                  5          0.00102495          49 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                 32                  3          0.0073498           61 │
│ trial_fa758    RUNNING            5   adam            relu                                   64                 32                  3          0.000219563         45 │
│ trial_fa758    RUNNING            6   rmsprop         tanh                                   32                128                  3          0.000110116         75 │
│ trial_fa758    RUNNING            6   adam            relu                                   64                128                  3          0.00500405          50 │
│ trial_fa758    RUNNING            6   rmsprop         relu                                   32                256                  5          7.32936e-05         76 │
│ trial_fa758    RUNNING            5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31 │
│ trial_fa758    RUNNING            4   adam            tanh                                   64                256                  5          0.000389976         42 │
│ trial_fa758    RUNNING            8   adam            relu                                   64                 32                  3          1.00577e-05         65 │
│ trial_fa758    RUNNING            4   adam            relu                                  128                256                  3          0.000235443         92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m103/274[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m32s[0m 190ms/step - accuracy: 0.3894 - loss: 1.5586[32m [repeated 260x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m158/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1:13[0m 189ms/step - accuracy: 0.1401 - loss: 2.6184[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m36s[0m 132ms/step - accuracy: 0.2523 - loss: 1.9800 - val_accuracy: 0.2877 - val_loss: 1.6967
[36m(train_cnn_ray_tune pid=571086)[0m Epoch 8/61
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 262ms/step - accuracy: 0.2500 - loss: 1.9805
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m534/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 63ms/step - accuracy: 0.1122 - loss: 2.7624
[1m535/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 63ms/step - accuracy: 0.1122 - loss: 2.7624[32m [repeated 31x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m533/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 63ms/step - accuracy: 0.1122 - loss: 2.7624[32m [repeated 62x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m25/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 42ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m29/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 41ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 40ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m36/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m38/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m42/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 42ms/step
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m39s[0m 72ms/step - accuracy: 0.1535 - loss: 2.4657 - val_accuracy: 0.2354 - val_loss: 2.0368
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 8/82
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m44/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m46/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m538/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 63ms/step - accuracy: 0.1123 - loss: 2.7624
[1m539/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 63ms/step - accuracy: 0.1123 - loss: 2.7624[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 63ms/step - accuracy: 0.1123 - loss: 2.7622[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:44[0m 191ms/step - accuracy: 0.2812 - loss: 2.2433
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 42ms/step
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:40[0m 184ms/step - accuracy: 0.0625 - loss: 2.6662
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 59ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 60ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 88/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m21s[0m 116ms/step - accuracy: 0.2564 - loss: 1.9555
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 120ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m  2/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 54ms/step  
[1m  4/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 56/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 58ms/step - accuracy: 0.1844 - loss: 2.3964
[1m 57/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 58ms/step - accuracy: 0.1842 - loss: 2.3966
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m  6/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m  7/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 56ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m  8/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 57ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 10/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 52ms/step
[1m 12/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 51ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 14/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 187ms/step - accuracy: 0.0722 - loss: 3.0071 - val_accuracy: 0.1087 - val_loss: 2.5818
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 24/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 43ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 31/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 32/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m5s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m5s[0m 376ms/step - accuracy: 0.2833 - loss: 1.9762[32m [repeated 131x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 34/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m5s[0m 47ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 38/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m5s[0m 45ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 40/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 45ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m40s[0m 73ms/step - accuracy: 0.1123 - loss: 2.7622 - val_accuracy: 0.2170 - val_loss: 2.2945
[36m(train_cnn_ray_tune pid=571061)[0m Epoch 6/31[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 42/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 44ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 47/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 53/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 56/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 61/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571069)[0m 
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m189/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m15s[0m 183ms/step - accuracy: 0.3875 - loss: 1.5638[32m [repeated 199x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[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=571069)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571083)[0m 2025-10-27 11:52:09.830797: 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=571083)[0m 2025-10-27 11:52:09.852582: 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=571083)[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=571083)[0m E0000 00:00:1761562329.879818  572385 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=571083)[0m E0000 00:00:1761562329.887866  572385 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=571083)[0m W0000 00:00:1761562329.908309  572385 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=571083)[0m 2025-10-27 11:52:09.914425: 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=571083)[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=571061)[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=571061)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 2025-10-27 11:52:13.398530: 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=571061)[0m 2025-10-27 11:52:13.398591: 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=571061)[0m 2025-10-27 11:52:13.398599: 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=571061)[0m 2025-10-27 11:52:13.398603: 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=571061)[0m 2025-10-27 11:52:13.398608: 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=571061)[0m 2025-10-27 11:52:13.398611: 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=571061)[0m 2025-10-27 11:52:13.398869: 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=571061)[0m 2025-10-27 11:52:13.398941: 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=571061)[0m 2025-10-27 11:52:13.398945: 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=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 4/97[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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[36m(train_cnn_ray_tune pid=571069)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 11:56:57. Total running time: 4min 51s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             288.099 │
│ time_total_s                 288.099 │
│ training_iteration                 1 │
│ val_accuracy                 0.10711 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 11:56:57. Total running time: 4min 51s
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 4/42[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 6/92

Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-10-27 11:57:07. Total running time: 5min 0s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                 32                  3          0.00903085          67                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m384/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m9s[0m 56ms/step - accuracy: 0.1698 - loss: 2.4110
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m96s[0m 175ms/step - accuracy: 0.1321 - loss: 2.7109 - val_accuracy: 0.1492 - val_loss: 2.3741
[36m(train_cnn_ray_tune pid=571082)[0m Epoch 4/75
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m272/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 107ms/step - accuracy: 0.2512 - loss: 1.9685
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 9/61[32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 3/70[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 3/76[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m Epoch 9/67[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m Epoch 4/49[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-10-27 11:57:37. Total running time: 5min 30s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                 32                  3          0.00903085          67                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 7/31
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 26/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 329ms/step - accuracy: 0.3081 - loss: 1.7887[32m [repeated 49x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 181ms/step - accuracy: 0.0759 - loss: 2.9843 - val_accuracy: 0.1180 - val_loss: 2.5312
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 13/37
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36s[0m 270ms/step - accuracy: 0.0547 - loss: 2.9448
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 5/39
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m Epoch 5/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 7/92
[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 4/38
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 11/45[32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-10-27 11:58:07. Total running time: 6min 1s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                 32                  3          0.00903085          67                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 3/64[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 78/274[0m [32m━━━━━[0m[37m━━�
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 55/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m28s[0m 346ms/step - accuracy: 0.3288 - loss: 1.7924[32m [repeated 213x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m �━━━━━━━━━━━━[0m [1m22s[0m 116ms/step - accuracy: 0.1238 - loss: 2.3663
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 8/31
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 11/61[32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 5/42
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:31[0m 555ms/step - accuracy: 0.3750 - loss: 1.5589
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m110/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m9s[0m 347ms/step - accuracy: 0.3288 - loss: 1.7977[32m [repeated 53x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m32s[0m 117ms/step - accuracy: 0.1759 - loss: 2.2891 - val_accuracy: 0.2107 - val_loss: 1.9661
[36m(train_cnn_ray_tune pid=571070)[0m Epoch 12/45
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 219ms/step - accuracy: 0.2344 - loss: 2.2297
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m37s[0m 68ms/step - accuracy: 0.1853 - loss: 2.3116 - val_accuracy: 0.2607 - val_loss: 1.8979
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 40ms/step - accuracy: 0.1319 - loss: 2.2576  
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m129/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m16s[0m 114ms/step - accuracy: 0.2647 - loss: 1.8869
[1m130/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m16s[0m 114ms/step - accuracy: 0.2647 - loss: 1.8869[32m [repeated 9x across cluster][0m
Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-10-27 11:58:37. Total running time: 6min 31s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                 32                  3          0.00903085          67                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 39/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 56ms/step - accuracy: 0.1706 - loss: 2.2773[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 11/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:39[0m 182ms/step - accuracy: 0.1250 - loss: 2.3169
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m18s[0m 495ms/step - accuracy: 0.0888 - loss: 3.0008[32m [repeated 245x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m90s[0m 165ms/step - accuracy: 0.1268 - loss: 2.6400 - val_accuracy: 0.1532 - val_loss: 2.3218
[36m(train_cnn_ray_tune pid=571082)[0m Epoch 5/75
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m122/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1:04[0m 151ms/step - accuracy: 0.2837 - loss: 1.8711
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 73/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 55ms/step - accuracy: 0.1758 - loss: 2.2858
[1m 74/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 55ms/step - accuracy: 0.1758 - loss: 2.2860[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m546/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 61ms/step - accuracy: 0.1298 - loss: 2.6804[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m539/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 61ms/step - accuracy: 0.1298 - loss: 2.6803
[1m540/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 61ms/step - accuracy: 0.1298 - loss: 2.6803[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m178/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m10s[0m 112ms/step - accuracy: 0.2651 - loss: 1.8887
[1m179/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m10s[0m 112ms/step - accuracy: 0.2651 - loss: 1.8888[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 1s/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 3/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 27ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 5/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m219/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 177ms/step - accuracy: 0.4087 - loss: 1.4955[32m [repeated 82x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 8/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 35ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m11/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m13/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m14/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 49ms/step
[1m18/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m20/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 45ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 43ms/step
[1m24/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m26/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m461/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m10s[0m 125ms/step - accuracy: 0.1853 - loss: 2.5826[32m [repeated 241x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m28/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m37s[0m 134ms/step - accuracy: 0.1232 - loss: 2.3567 - val_accuracy: 0.1354 - val_loss: 2.2010
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m29/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 43ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m36/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m38/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m40/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m42/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m47/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 40ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m49/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m173/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m19s[0m 52ms/step - accuracy: 0.1808 - loss: 2.2955
[1m174/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m19s[0m 52ms/step - accuracy: 0.1809 - loss: 2.2955[32m [repeated 21x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m52/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 42ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m55/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 75/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1:39[0m 501ms/step - accuracy: 0.1757 - loss: 2.3901[32m [repeated 43x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m66/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 43ms/step
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m124/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 102ms/step - accuracy: 0.1904 - loss: 2.2423
[1m125/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 102ms/step - accuracy: 0.1904 - loss: 2.2424[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m42s[0m 185ms/step - accuracy: 0.0739 - loss: 2.9848 - val_accuracy: 0.1217 - val_loss: 2.4908
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 15/37
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 320ms/step - accuracy: 0.0703 - loss: 2.8569
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 63ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 64ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m44s[0m 80ms/step - accuracy: 0.1298 - loss: 2.6804 - val_accuracy: 0.2051 - val_loss: 2.2249
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m496/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m6s[0m 125ms/step - accuracy: 0.1853 - loss: 2.5822[32m [repeated 149x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 141ms/step
[1m  3/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 37ms/step  
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m  5/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 40ms/step
[1m  7/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m  9/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 11/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 38ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 13/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m248/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 52ms/step - accuracy: 0.1849 - loss: 2.2936
[1m249/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 52ms/step - accuracy: 0.1849 - loss: 2.2936
[1m250/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 52ms/step - accuracy: 0.1850 - loss: 2.2935
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 15/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 40ms/step
[1m 17/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 19/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m381/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m37s[0m 225ms/step - accuracy: 0.1606 - loss: 2.5917[32m [repeated 197x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 36/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m5s[0m 44ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 39/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m5s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 41/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m262/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 109ms/step - accuracy: 0.2655 - loss: 1.8926
[1m263/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 109ms/step - accuracy: 0.2655 - loss: 1.8926[32m [repeated 30x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 43/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 43ms/step
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 47/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 48/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 52/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 46ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 55/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 46ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 46ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 65/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 67/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 47ms/step
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m168/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m10s[0m 101ms/step - accuracy: 0.1889 - loss: 2.2467
[1m169/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m10s[0m 101ms/step - accuracy: 0.1889 - loss: 2.2467[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 71/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 47ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 75/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 46ms/step
[1m 76/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m53s[0m 388ms/step - accuracy: 0.3290 - loss: 1.7984 - val_accuracy: 0.4085 - val_loss: 1.4475
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 11/69[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 77/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 46ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 80/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 46ms/step
[1m 81/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 83/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 372ms/step - accuracy: 0.3750 - loss: 1.6889[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 84/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 46ms/step
[1m 86/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 88/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 92/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 95/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 45ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 45ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m 99/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571076)[0m 
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[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=571076)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
[1m158/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571076)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 11:58:54. Total running time: 6min 48s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             405.652 │
│ time_total_s                 405.652 │
│ training_iteration                 1 │
│ val_accuracy                 0.13538 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571043)[0m 
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Trial trial_fa758 completed after 1 iterations at 2025-10-27 11:58:54. Total running time: 6min 48s
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 12/61
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m Epoch 8/50
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 9/31
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 11:59:07. Total running time: 7min 1s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m Epoch 6/82[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 12/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 12/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 13/61
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:52:34[0m 52s/step - accuracy: 0.0938 - loss: 2.9576
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 14/45
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 4/76[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 11:59:37. Total running time: 7min 31s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 6/97[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 6/42[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 13/69
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 18/37[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 15/45
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 278ms/step - accuracy: 0.2344 - loss: 2.1332
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:00:07. Total running time: 8min 1s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 7/39
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 14/82
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 283ms/step - accuracy: 0.0703 - loss: 2.9558
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 19/37
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 10/92
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 14/69[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m25s[0m 256ms/step - accuracy: 0.1338 - loss: 2.7362[32m [repeated 261x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m Epoch 16/45[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:00:37. Total running time: 8min 31s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m Epoch 6/75
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 16/61
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 17/45[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:07[0m 234ms/step - accuracy: 0.2188 - loss: 2.7211[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:01:07. Total running time: 9min 1s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 21/37[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 6/38
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m Epoch 6/49[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 16/82[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 18/45[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:01:37. Total running time: 9min 31s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 85/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m17s[0m 91ms/step - accuracy: 0.2291 - loss: 2.0495[32m [repeated 123x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 5/70
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 16/69
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 5/76
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 11/92
[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 13/31
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 500ms/step - accuracy: 0.1797 - loss: 2.5562
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m175/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m16s[0m 167ms/step - accuracy: 0.4327 - loss: 1.4117[32m [repeated 237x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 181ms/step - accuracy: 0.0885 - loss: 2.8643 - val_accuracy: 0.1708 - val_loss: 2.3794
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 23/37
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 363ms/step - accuracy: 0.0781 - loss: 2.7644
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m273/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 103ms/step - accuracy: 0.2749 - loss: 1.8658[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m31s[0m 114ms/step - accuracy: 0.2250 - loss: 2.0632 - val_accuracy: 0.2775 - val_loss: 1.7424
[36m(train_cnn_ray_tune pid=571070)[0m Epoch 19/45
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 17/82[32m [repeated 2x across cluster][0m
Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:02:07. Total running time: 10min 1s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 17/69
[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 8/97[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m Epoch 7/75
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 8/42
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:46[0m 388ms/step - accuracy: 0.3750 - loss: 1.5406
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 14/31
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m32s[0m 117ms/step - accuracy: 0.2734 - loss: 1.8598 - val_accuracy: 0.3101 - val_loss: 1.6507
[36m(train_cnn_ray_tune pid=571086)[0m Epoch 19/61
[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:02:37. Total running time: 10min 31s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 12/92[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 9/39[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m Epoch 25/37
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 365ms/step - accuracy: 0.1250 - loss: 2.7453
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m37s[0m 67ms/step - accuracy: 0.1528 - loss: 2.5459 - val_accuracy: 0.2249 - val_loss: 2.0930
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 18/69
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:38[0m 181ms/step - accuracy: 0.1562 - loss: 2.6564
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:58[0m 434ms/step - accuracy: 0.4062 - loss: 1.5697
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  4/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 275ms/step - accuracy: 0.4134 - loss: 1.5352
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  5/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 274ms/step - accuracy: 0.4176 - loss: 1.5305
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 5/64[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m 92/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m25s[0m 56ms/step - accuracy: 0.1685 - loss: 2.5194[32m [repeated 166x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 7/38
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:03:07. Total running time: 11min 1s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 34/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 313ms/step - accuracy: 0.4278 - loss: 1.5317[32m [repeated 53x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m32s[0m 117ms/step - accuracy: 0.2795 - loss: 1.8658 - val_accuracy: 0.2970 - val_loss: 1.6577
[36m(train_cnn_ray_tune pid=571086)[0m Epoch 20/61
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 293ms/step - accuracy: 0.2656 - loss: 1.8095
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[1m470/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m4s[0m 58ms/step - accuracy: 0.2384 - loss: 2.0478[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[1m291/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m15s[0m 59ms/step - accuracy: 0.1615 - loss: 2.5295[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m 57/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1:24[0m 173ms/step - accuracy: 0.2515 - loss: 2.1874[32m [repeated 57x across cluster][0m
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m52s[0m 190ms/step - accuracy: 0.4278 - loss: 1.4315 - val_accuracy: 0.4328 - val_loss: 1.3998
[36m(train_cnn_ray_tune pid=571083)[0m Epoch 13/50
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:36[0m 353ms/step - accuracy: 0.4062 - loss: 1.5355
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m27s[0m 199ms/step - accuracy: 0.0964 - loss: 2.8225 - val_accuracy: 0.1573 - val_loss: 2.3702
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 26/37
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 306ms/step - accuracy: 0.1094 - loss: 2.7520
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 67/274[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1:04[0m 311ms/step - accuracy: 0.4258 - loss: 1.5372[32m [repeated 56x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m44s[0m 319ms/step - accuracy: 0.1438 - loss: 2.6490 - val_accuracy: 0.1636 - val_loss: 2.2561
[36m(train_cnn_ray_tune pid=571061)[0m Epoch 15/31[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 384ms/step - accuracy: 0.1562 - loss: 2.5902[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m6s[0m 341ms/step - accuracy: 0.4052 - loss: 1.5671[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 91/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m22s[0m 484ms/step - accuracy: 0.1151 - loss: 2.8489[32m [repeated 305x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m436/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m6s[0m 59ms/step - accuracy: 0.1583 - loss: 2.5310
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m38s[0m 69ms/step - accuracy: 0.2387 - loss: 2.0479 - val_accuracy: 0.3132 - val_loss: 1.6721
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m442/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m6s[0m 59ms/step - accuracy: 0.1583 - loss: 2.5310
[1m443/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m6s[0m 59ms/step - accuracy: 0.1583 - loss: 2.5309[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:29[0m 164ms/step - accuracy: 0.1562 - loss: 2.0321
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 11/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34s[0m 65ms/step - accuracy: 0.2073 - loss: 1.9997
[1m 12/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 67ms/step - accuracy: 0.2086 - loss: 1.9999[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 19/82
[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 13/92
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m492/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m7s[0m 145ms/step - accuracy: 0.1594 - loss: 2.4677
[1m493/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m7s[0m 145ms/step - accuracy: 0.1594 - loss: 2.4676[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 19/69
Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:03:37. Total running time: 11min 31s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1:38[0m 506ms/step - accuracy: 0.2426 - loss: 2.0457[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 184ms/step - accuracy: 0.0983 - loss: 2.8187 - val_accuracy: 0.1510 - val_loss: 2.3685
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 27/37
[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 21/61
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 20/82[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m139/274[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 74ms/step - accuracy: 0.2392 - loss: 2.0227 
[1m140/274[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 74ms/step - accuracy: 0.2392 - loss: 2.0227
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:24[0m 1s/step
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 151ms/step - accuracy: 0.1022 - loss: 2.7988 - val_accuracy: 0.1494 - val_loss: 2.3705[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 28/37[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m 2/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 67ms/step
[1m 4/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:11[0m 481ms/step - accuracy: 0.3281 - loss: 1.7815[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 36ms/step
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 37ms/step
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m14/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m20/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 48ms/step
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 46ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m46/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 46ms/step
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m55/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 49ms/step
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 20/69
[36m(train_cnn_ray_tune pid=571083)[0m 
[1m  2/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 54ms/step  
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-27 12:04:07. Total running time: 12min 1s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                128                  3          0.00500405          50                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m 90/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 50ms/step
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m114/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 50ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[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=571083)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m138/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 50ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 22/61
[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m142/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 49ms/step
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[36m(train_cnn_ray_tune pid=571083)[0m 
[1m146/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 50ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571083)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:04:13. Total running time: 12min 6s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             723.579 │
│ time_total_s                 723.579 │
│ training_iteration                 1 │
│ val_accuracy                 0.45573 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:04:13. Total running time: 12min 6s
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 23/45
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[1m219/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m40s[0m 125ms/step - accuracy: 0.2321 - loss: 2.3251[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m24s[0m 177ms/step - accuracy: 0.1028 - loss: 2.7960 - val_accuracy: 0.1443 - val_loss: 2.3720
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 29/37
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 313ms/step - accuracy: 0.1250 - loss: 2.8120
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m24s[0m 453ms/step - accuracy: 0.1141 - loss: 2.8420[32m [repeated 218x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m376/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m9s[0m 58ms/step - accuracy: 0.1564 - loss: 2.5019 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  4/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 303ms/step - accuracy: 0.3926 - loss: 1.5000[32m [repeated 36x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m11s[0m 105ms/step - accuracy: 0.2802 - loss: 1.8445
[1m162/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m11s[0m 105ms/step - accuracy: 0.2802 - loss: 1.8445[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m90s[0m 329ms/step - accuracy: 0.4213 - loss: 1.5331 - val_accuracy: 0.4267 - val_loss: 1.4533
[36m(train_cnn_ray_tune pid=571092)[0m Epoch 8/49
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:55[0m 421ms/step - accuracy: 0.3594 - loss: 1.5227
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m284/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m36s[0m 140ms/step - accuracy: 0.1717 - loss: 2.4101[32m [repeated 223x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m189/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m8s[0m 105ms/step - accuracy: 0.2804 - loss: 1.8446
[1m190/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m8s[0m 104ms/step - accuracy: 0.2804 - loss: 1.8446[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m186/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m8s[0m 98ms/step - accuracy: 0.2451 - loss: 1.9829
[1m187/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m8s[0m 98ms/step - accuracy: 0.2451 - loss: 1.9829[32m [repeated 21x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m518/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 57ms/step - accuracy: 0.1569 - loss: 2.5001[32m [repeated 50x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m187/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1:17[0m 215ms/step - accuracy: 0.1939 - loss: 2.3652[32m [repeated 53x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m397/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m18s[0m 122ms/step - accuracy: 0.2977 - loss: 1.8080
[1m398/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m18s[0m 122ms/step - accuracy: 0.2977 - loss: 1.8080[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m41s[0m 74ms/step - accuracy: 0.2578 - loss: 2.0042 - val_accuracy: 0.3312 - val_loss: 1.6474
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 21/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:40[0m 294ms/step - accuracy: 0.1875 - loss: 1.9397
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 54ms/step - accuracy: 0.1875 - loss: 1.9491  
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m226/274[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m4s[0m 103ms/step - accuracy: 0.2802 - loss: 1.8455[32m [repeated 69x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m29s[0m 462ms/step - accuracy: 0.2448 - loss: 2.0314[32m [repeated 208x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 36/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 52ms/step - accuracy: 0.2163 - loss: 1.9933
[1m 37/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 53ms/step - accuracy: 0.2167 - loss: 1.9939[32m [repeated 12x across cluster][0m

Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-10-27 12:04:37. Total running time: 12min 31s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 21/69[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:09[0m 254ms/step - accuracy: 0.2656 - loss: 2.0982
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 8/38[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36s[0m 267ms/step - accuracy: 0.1328 - loss: 2.5648[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m Epoch 30/37
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 14/92
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 10/97
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m4s[0m 82ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m17/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m4s[0m 80ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m18/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m4s[0m 81ms/step
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 81ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m20/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 80ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m21/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 81ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m23/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 80ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m24/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 80ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m485/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 55ms/step - accuracy: 0.2495 - loss: 1.9942
[1m486/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 55ms/step - accuracy: 0.2495 - loss: 1.9942
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m26/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 78ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m27/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 79ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m28/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 78ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 77ms/step
[1m31/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 76ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m501/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m6s[0m 140ms/step - accuracy: 0.1714 - loss: 2.4023
[1m502/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m6s[0m 140ms/step - accuracy: 0.1714 - loss: 2.4023[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 74ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m34/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 74ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m35/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 74ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m  8/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 141ms/step - accuracy: 0.3011 - loss: 1.8038
[1m  9/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 136ms/step - accuracy: 0.3020 - loss: 1.8036
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m36/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 75ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m500/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 55ms/step - accuracy: 0.2497 - loss: 1.9940
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 76ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 75ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 75ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m46/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 75ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m47/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 75ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 75ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m77s[0m 140ms/step - accuracy: 0.2985 - loss: 1.8019 - val_accuracy: 0.3306 - val_loss: 1.5894
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m49/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 76ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 77ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 19/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:07[0m 128ms/step - accuracy: 0.3022 - loss: 1.7990[32m [repeated 48x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 77ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-10-27 12:05:07. Total running time: 13min 1s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              6   adam            relu                                  128                256                  3          1.69169e-05         82                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 27/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m7s[0m 60ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 33/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m7s[0m 58ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m73s[0m 134ms/step - accuracy: 0.2373 - loss: 2.3141 - val_accuracy: 0.3298 - val_loss: 1.8221
[36m(train_cnn_ray_tune pid=571081)[0m Epoch 11/39
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 34/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m7s[0m 58ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 35/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m7s[0m 58ms/step
[1m 36/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m7s[0m 58ms/step
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m160/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m59s[0m 155ms/step - accuracy: 0.2606 - loss: 2.1137 [32m [repeated 60x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 56/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m58s[0m 119ms/step - accuracy: 0.2989 - loss: 1.7897
[1m 57/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 118ms/step - accuracy: 0.2989 - loss: 1.7897
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 64/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 119ms/step - accuracy: 0.2989 - loss: 1.7894
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 38/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 57ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:56[0m 213ms/step - accuracy: 0.1875 - loss: 2.2048
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 44/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m6s[0m 56ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m35s[0m 63ms/step - accuracy: 0.2502 - loss: 1.9935 - val_accuracy: 0.3259 - val_loss: 1.6366
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 45/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m6s[0m 56ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 47/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m6s[0m 56ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m6s[0m 57ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 58/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m5s[0m 57ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m5s[0m 57ms/step
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m5s[0m 57ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 84/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 59ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 87/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 59ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 89/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m4s[0m 58ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 90/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m4s[0m 58ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 91/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 59ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 92/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 59ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 52/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 48ms/step - accuracy: 0.2610 - loss: 1.9865
[1m 53/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 49ms/step - accuracy: 0.2608 - loss: 1.9864[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 93/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 59ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 94/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 60ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 95/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 59ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 96/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 60ms/step
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 60ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  8/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 97ms/step - accuracy: 0.2656 - loss: 1.9310 
[1m  9/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 97ms/step - accuracy: 0.2645 - loss: 1.9333
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 98/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 60ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m 99/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 60ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m100/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 60ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m102/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 60ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m104/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 60ms/step
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m106/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m107/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
[1m110/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m112/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m113/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m115/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m23s[0m 165ms/step - accuracy: 0.1080 - loss: 2.7581 - val_accuracy: 0.1399 - val_loss: 2.3743[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m Epoch 25/45[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m116/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 15/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:01[0m 238ms/step - accuracy: 0.3715 - loss: 1.6018[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m105/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m50s[0m 115ms/step - accuracy: 0.2994 - loss: 1.7917
[1m106/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m50s[0m 115ms/step - accuracy: 0.2994 - loss: 1.7917[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m118/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571079)[0m 
[1m119/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 61ms/step
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:35[0m 175ms/step - accuracy: 0.2500 - loss: 2.0225
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571079)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:05:17. Total running time: 13min 11s
[36m(train_cnn_ray_tune pid=571079)[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=571079)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             788.259 │
│ time_total_s                 788.259 │
│ training_iteration                 1 │
│ val_accuracy                 0.17055 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:05:17. Total running time: 13min 11s
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 18/31[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-10-27 12:05:37. Total running time: 13min 31s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571093)[0m 
[1m52/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 35ms/step
[1m53/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 36ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m358/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m10s[0m 53ms/step - accuracy: 0.1617 - loss: 2.4508
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 37ms/step
[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 15/92
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 50/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1:38[0m 441ms/step - accuracy: 0.2730 - loss: 1.9715[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[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=571093)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 23/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
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[36m(train_cnn_ray_tune pid=571093)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 35ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 35ms/step

Trial trial_fa758 finished iteration 1 at 2025-10-27 12:05:45. Total running time: 13min 39s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             816.895 │
│ time_total_s                 816.895 │
│ training_iteration                 1 │
│ val_accuracy                 0.13834 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:05:45. Total running time: 13min 39s
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 72/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m15s[0m 78ms/step - accuracy: 0.2441 - loss: 1.9722
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 23/69[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m Epoch 9/49
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 19/31[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 363ms/step - accuracy: 0.1484 - loss: 2.5539
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m24s[0m 86ms/step - accuracy: 0.2527 - loss: 1.9584 - val_accuracy: 0.3176 - val_loss: 1.6445
[36m(train_cnn_ray_tune pid=571070)[0m Epoch 27/45
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 129ms/step - accuracy: 0.2656 - loss: 1.8711
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-10-27 12:06:07. Total running time: 14min 1s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[1m473/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 42ms/step - accuracy: 0.1698 - loss: 2.4436[32m [repeated 52x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 11/97[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:53[0m 208ms/step - accuracy: 0.4375 - loss: 1.4219[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m69s[0m 126ms/step - accuracy: 0.3000 - loss: 1.7855 - val_accuracy: 0.3267 - val_loss: 1.5661
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m Epoch 16/92[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 137ms/step - accuracy: 0.0938 - loss: 2.5670[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 12/39
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 51/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 182ms/step - accuracy: 0.2159 - loss: 2.2735[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m Epoch 10/75
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:55[0m 212ms/step - accuracy: 0.0625 - loss: 2.4182
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 90/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m48s[0m 107ms/step - accuracy: 0.2350 - loss: 2.2330
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 189ms/step - accuracy: 0.2500 - loss: 1.7130
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 28/45[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-10-27 12:06:37. Total running time: 14min 31s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 25/82
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 210ms/step - accuracy: 0.2656 - loss: 1.7280
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 27/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 83ms/step - accuracy: 0.2780 - loss: 1.7995[32m [repeated 46x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m122/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m52s[0m 123ms/step - accuracy: 0.1927 - loss: 2.2812
[1m123/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m52s[0m 123ms/step - accuracy: 0.1927 - loss: 2.2811[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m301/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m26s[0m 107ms/step - accuracy: 0.3115 - loss: 1.7368[32m [repeated 256x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m544/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 48ms/step - accuracy: 0.1707 - loss: 2.4290
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m161/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 88ms/step - accuracy: 0.2531 - loss: 1.9129 
[1m162/274[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 88ms/step - accuracy: 0.2531 - loss: 1.9129
[36m(train_cnn_ray_tune pid=571086)[0m 
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[1m 48/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m18s[0m 83ms/step - accuracy: 0.2839 - loss: 1.7931[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m28s[0m 101ms/step - accuracy: 0.2865 - loss: 1.8261 - val_accuracy: 0.3332 - val_loss: 1.5849
[36m(train_cnn_ray_tune pid=571086)[0m Epoch 27/61
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m171/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 89ms/step - accuracy: 0.2533 - loss: 1.9132[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 25/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m27s[0m 100ms/step - accuracy: 0.2548 - loss: 1.9145 - val_accuracy: 0.3130 - val_loss: 1.6408
[36m(train_cnn_ray_tune pid=571070)[0m Epoch 29/45
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:06[0m 245ms/step - accuracy: 0.1719 - loss: 1.8078
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m5s[0m 88ms/step - accuracy: 0.2898 - loss: 1.7888
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m224/274[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m4s[0m 88ms/step - accuracy: 0.2898 - loss: 1.7895[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m  2/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 296ms/step - accuracy: 0.3984 - loss: 1.5368 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m Epoch 17/92
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:01[0m 451ms/step - accuracy: 0.3984 - loss: 1.5639
[36m(train_cnn_ray_tune pid=571092)[0m Epoch 10/49
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:40[0m 366ms/step - accuracy: 0.5312 - loss: 1.4082
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 12/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:06[0m 253ms/step - accuracy: 0.4560 - loss: 1.4459[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m Epoch 21/31
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 28/61
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-10-27 12:07:08. Total running time: 15min 1s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m366/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m8s[0m 47ms/step - accuracy: 0.2593 - loss: 1.9254
[1m367/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m8s[0m 47ms/step - accuracy: 0.2593 - loss: 1.9254[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 7/64
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 13/39[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 12/42[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 8/70
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-10-27 12:07:38. Total running time: 15min 31s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         tanh                                   32                128                  3          0.000110116         75                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m Epoch 29/61[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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r][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 83/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m48s[0m 105ms/step - accuracy: 0.3167 - loss: 1.7680[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:31[0m 1s/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 2/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 50ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 4/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 43ms/step
[1m 7/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 45ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 8/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 47ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 44ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m14/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m17/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m20/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step
[1m21/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 49ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m24/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m26/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m27/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m29/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 49ms/step
[1m31/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m499/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m8s[0m 174ms/step - accuracy: 0.2214 - loss: 2.2300[32m [repeated 89x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m32/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m212/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m35s[0m 107ms/step - accuracy: 0.3096 - loss: 1.7673
[1m213/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m35s[0m 107ms/step - accuracy: 0.3096 - loss: 1.7672[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m34/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m36/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m38/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 49ms/step
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 49ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m41/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 48ms/step
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m228/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m34s[0m 108ms/step - accuracy: 0.3096 - loss: 1.7666[32m [repeated 129x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571068)[0m Epoch 18/92[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m47/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 48ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m49/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 48ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m52/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 48ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 49ms/step
[1m55/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 49ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m488/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 46ms/step - accuracy: 0.2707 - loss: 1.9207
[1m490/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 46ms/step - accuracy: 0.2707 - loss: 1.9207[32m [repeated 39x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 387ms/step - accuracy: 0.4688 - loss: 1.4725[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m73s[0m 133ms/step - accuracy: 0.1945 - loss: 2.2633 - val_accuracy: 0.2055 - val_loss: 2.5450[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 49ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 48ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 61ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 62ms/step
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 27/69
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 43ms/step
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 31/45
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 77/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 45ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 79/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m28s[0m 100ms/step - accuracy: 0.2681 - loss: 1.9109 - val_accuracy: 0.3247 - val_loss: 1.6239
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 84/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 44ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 93/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 44ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 44ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 43ms/step
[1m102/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 44ms/step
[1m104/159[0m [32m�
[36m(train_cnn_ray_tune pid=571082)[0m �━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m 52/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 332ms/step - accuracy: 0.3066 - loss: 1.8230[32m [repeated 47x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m30s[0m 55ms/step - accuracy: 0.2709 - loss: 1.9202 - val_accuracy: 0.3344 - val_loss: 1.6005
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m106/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 44ms/step
[36m(train_cnn_ray_tune pid=571082)[0m 
[1m108/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 44ms/step
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 167ms/step - accuracy: 0.1562 - loss: 2.1260
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
[1m115/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 44ms/step
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[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=571082)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571082)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:07:53. Total running time: 15min 47s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             943.883 │
│ time_total_s                 943.883 │
│ training_iteration                 1 │
│ val_accuracy                 0.20553 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:07:53. Total running time: 15min 47s
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 27/82
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:44:19[0m 11s/step - accuracy: 0.1875 - loss: 2.2864
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m120/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m56s[0m 366ms/step - accuracy: 0.2990 - loss: 1.8721[32m [repeated 181x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m273/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 84ms/step - accuracy: 0.2907 - loss: 1.8216
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 84ms/step - accuracy: 0.2907 - loss: 1.8216[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:50[0m 1s/step
[36m(train_cnn_ray_tune pid=571086)[0m 
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[1m 5/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 30ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 7/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
[1m 9/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m126s[0m 195ms/step - accuracy: 0.2217 - loss: 2.2281 - val_accuracy: 0.2642 - val_loss: 1.9038
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 8/76
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 35ms/step
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 31ms/step
[1m17/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 31ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:17[0m 253ms/step - accuracy: 0.1562 - loss: 2.3055
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 31ms/step
[1m21/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 31ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m24/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m26/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m155/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - accuracy: 0.1812 - loss: 2.3780[32m [repeated 140x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m28/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 32ms/step
[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m412/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m14s[0m 107ms/step - accuracy: 0.3099 - loss: 1.7586
[1m413/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m14s[0m 107ms/step - accuracy: 0.3099 - loss: 1.7586[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m31/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 33ms/step
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 33ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m34/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m36/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m41/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m135/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m50s[0m 363ms/step - accuracy: 0.2981 - loss: 1.8717[32m [repeated 151x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 34ms/step
[1m47/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m49/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m53/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 34ms/step
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 33ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m188/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m6s[0m 77ms/step - accuracy: 0.2729 - loss: 1.9139
[1m189/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m6s[0m 77ms/step - accuracy: 0.2729 - loss: 1.9138[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m62/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 34ms/step
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m66/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 33ms/step
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 34ms/step
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m299/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m10s[0m 44ms/step - accuracy: 0.2727 - loss: 1.9202
[1m300/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m10s[0m 44ms/step - accuracy: 0.2727 - loss: 1.9202[32m [repeated 56x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 34ms/step
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 49ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 50ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 21/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 171ms/step - accuracy: 0.2610 - loss: 2.1557[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 117ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m  3/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step  
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m  6/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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Trial status: 14 RUNNING | 6 TERMINATED
Current time: 2025-10-27 12:08:08. Total running time: 16min 1s
Logical resource usage: 14.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   adam            relu                                   64                 32                  3          0.0073498           61                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    RUNNING              4   adam            relu                                  128                256                  3          0.000235443         92                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 65/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m149/274[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m45s[0m 362ms/step - accuracy: 0.2972 - loss: 1.8715[32m [repeated 111x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 67/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 32ms/step
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 71/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 32ms/step
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 78/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 32ms/step
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 81/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m 83/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m251/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 78ms/step - accuracy: 0.2725 - loss: 1.9104
[1m252/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 78ms/step - accuracy: 0.2725 - loss: 1.9103[32m [repeated 26x across cluster][0m
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[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=571086)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
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[36m(train_cnn_ray_tune pid=571086)[0m 
[1m158/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 33ms/step
[36m(train_cnn_ray_tune pid=571086)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 33ms/step

Trial trial_fa758 finished iteration 1 at 2025-10-27 12:08:11. Total running time: 16min 5s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             962.373 │
│ time_total_s                 962.373 │
│ training_iteration                 1 │
│ val_accuracy                 0.31798 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:08:11. Total running time: 16min 5s
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 23/31
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m Epoch 11/49[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m39s[0m 285ms/step - accuracy: 0.4678 - loss: 1.3733 - val_accuracy: 0.4664 - val_loss: 1.3720
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 294ms/step - accuracy: 0.5625 - loss: 1.2989
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m349/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m25s[0m 129ms/step - accuracy: 0.2752 - loss: 2.0131
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m524/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 44ms/step - accuracy: 0.1764 - loss: 2.3814
[1m526/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 44ms/step - accuracy: 0.1765 - loss: 2.3813[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m60s[0m 109ms/step - accuracy: 0.2685 - loss: 2.1830 - val_accuracy: 0.3599 - val_loss: 1.7200
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 779ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:26[0m 159ms/step - accuracy: 0.0938 - loss: 2.6507
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 3/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 27ms/step  
[1m 5/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 79/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m13s[0m 69ms/step - accuracy: 0.2778 - loss: 1.8736
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m13s[0m 69ms/step - accuracy: 0.2777 - loss: 1.8738[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 37ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 86/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m12s[0m 68ms/step - accuracy: 0.2770 - loss: 1.8750
[1m 87/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m12s[0m 68ms/step - accuracy: 0.2769 - loss: 1.8752
[1m 88/274[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m12s[0m 67ms/step - accuracy: 0.2768 - loss: 1.8755
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 8/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 38ms/step
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 38ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m11/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step
[1m13/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m17/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 41ms/step
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m21/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m23/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 41ms/step
[1m25/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m542/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 43ms/step - accuracy: 0.1765 - loss: 2.3811[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m27/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m29/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 41ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m35/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m37/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 41ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m119/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1:07[0m 157ms/step - accuracy: 0.2447 - loss: 2.1600[32m [repeated 39x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 40ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 41ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 41ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 41ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m384/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m20s[0m 127ms/step - accuracy: 0.2751 - loss: 2.0123
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 48ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 49ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 28/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 104ms/step
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m393/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m19s[0m 127ms/step - accuracy: 0.2751 - loss: 2.0121[32m [repeated 133x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m  3/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 31ms/step  
[1m  5/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 34ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m  7/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 37ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m  8/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 52ms/step - accuracy: 0.1250 - loss: 2.4207  
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 55ms/step - accuracy: 0.1319 - loss: 2.4463
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 10/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m397/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m18s[0m 126ms/step - accuracy: 0.2752 - loss: 2.0120
[1m398/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m18s[0m 126ms/step - accuracy: 0.2752 - loss: 2.0120
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 12/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 40ms/step
[1m 14/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 15/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m145/274[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m8s[0m 67ms/step - accuracy: 0.2750 - loss: 1.8831
[1m146/274[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m8s[0m 67ms/step - accuracy: 0.2750 - loss: 1.8831[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 17/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 43ms/step
[1m 19/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m39s[0m 50ms/step - accuracy: 0.1765 - loss: 2.3811 - val_accuracy: 0.2490 - val_loss: 1.9323[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m66s[0m 120ms/step - accuracy: 0.3099 - loss: 1.7558 - val_accuracy: 0.3308 - val_loss: 1.5604
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 21/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 40ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 24/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:37[0m 179ms/step - accuracy: 0.0938 - loss: 2.3562[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 26/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 41ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m121/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m17s[0m 40ms/step - accuracy: 0.2685 - loss: 1.9219
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 28/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 41ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 40/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 39ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 47/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 38ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 38ms/step
[1m 53/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 38ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m172/274[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m6s[0m 68ms/step - accuracy: 0.2749 - loss: 1.8838[32m [repeated 26x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 55/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 38ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 57/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 38ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 61/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[1m 65/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 67/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m  2/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 181ms/step - accuracy: 0.4492 - loss: 1.3248 [32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 71/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[1m 73/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 75/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 38ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 77/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[1m 79/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 81/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 83/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 39ms/step
[1m 85/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 87/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 89/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 40ms/step
[1m 91/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 93/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 39ms/step
[1m 95/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 96/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 39ms/step
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m 99/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m104/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 39ms/step
[1m105/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m107/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 39ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m109/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m111/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m112/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 40ms/step
[1m114/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m106/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m17s[0m 41ms/step - accuracy: 0.1749 - loss: 2.3898[32m [repeated 124x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m116/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 40ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 13/42[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m119/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m121/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 40ms/step
[1m123/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m125/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m127/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m22s[0m 351ms/step - accuracy: 0.2940 - loss: 1.8709[32m [repeated 157x across cluster][0m
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m129/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 40ms/step
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[36m(train_cnn_ray_tune pid=571068)[0m 
[1m132/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 40ms/step
[1m133/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m135/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m137/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m139/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
[1m140/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 40ms/step
[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571068)[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=571068)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571068)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:08:29. Total running time: 16min 23s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             980.774 │
│ time_total_s                 980.774 │
│ training_iteration                 1 │
│ val_accuracy                  0.4664 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:08:29. Total running time: 16min 23s
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 33/45
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-27 12:08:38. Total running time: 16min 32s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m29s[0m 214ms/step - accuracy: 0.1605 - loss: 2.4849 - val_accuracy: 0.1937 - val_loss: 2.1567
[36m(train_cnn_ray_tune pid=571061)[0m Epoch 24/31
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 297ms/step - accuracy: 0.1641 - loss: 2.4682
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m207/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m32s[0m 96ms/step - accuracy: 0.3134 - loss: 1.7353
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 47ms/step - accuracy: 0.2768 - loss: 1.8963 - val_accuracy: 0.3346 - val_loss: 1.5933
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m539/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 41ms/step - accuracy: 0.1795 - loss: 2.3720[32m [repeated 46x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 43ms/step - accuracy: 0.2292 - loss: 1.8809  
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 29/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:27[0m 160ms/step - accuracy: 0.2188 - loss: 1.8905
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m74s[0m 136ms/step - accuracy: 0.2760 - loss: 2.0076 - val_accuracy: 0.3674 - val_loss: 1.6128
[36m(train_cnn_ray_tune pid=571049)[0m Epoch 11/38
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:54[0m 210ms/step - accuracy: 0.3438 - loss: 2.0789
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m357/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m15s[0m 81ms/step - accuracy: 0.2670 - loss: 2.1513[32m [repeated 116x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 34/137[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m18s[0m 178ms/step - accuracy: 0.1699 - loss: 2.4728[32m [repeated 140x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m200/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 66ms/step - accuracy: 0.2743 - loss: 1.8797
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 47ms/step - accuracy: 0.1795 - loss: 2.3718 - val_accuracy: 0.2536 - val_loss: 1.9220
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m218/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m3s[0m 67ms/step - accuracy: 0.2744 - loss: 1.8802[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 34ms/step - accuracy: 0.1528 - loss: 2.3790  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 29/69
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 136ms/step - accuracy: 0.1562 - loss: 2.3224
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m195/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m13s[0m 39ms/step - accuracy: 0.2688 - loss: 1.8782[32m [repeated 142x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m423/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 81ms/step - accuracy: 0.2677 - loss: 2.1506 
[1m424/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 80ms/step - accuracy: 0.2678 - loss: 2.1506
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m 54/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 106ms/step - accuracy: 0.2880 - loss: 2.0194
[1m 55/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 106ms/step - accuracy: 0.2878 - loss: 2.0194
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m430/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 80ms/step - accuracy: 0.2678 - loss: 2.1505
[1m431/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 80ms/step - accuracy: 0.2678 - loss: 2.1505[32m [repeated 23x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m 61/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 105ms/step - accuracy: 0.2869 - loss: 2.0188
[1m 62/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 105ms/step - accuracy: 0.2867 - loss: 2.0187
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m231/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m12s[0m 39ms/step - accuracy: 0.2701 - loss: 1.8767
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m456/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m7s[0m 80ms/step - accuracy: 0.2680 - loss: 2.1501[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m 77/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 104ms/step - accuracy: 0.2846 - loss: 2.0177
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 8/64
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m386/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m6s[0m 39ms/step - accuracy: 0.2732 - loss: 1.8755[32m [repeated 60x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 15/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:17[0m 299ms/step - accuracy: 0.2833 - loss: 1.9362
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 34/45
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 9/70
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m44s[0m 162ms/step - accuracy: 0.2969 - loss: 1.8363
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-27 12:09:08. Total running time: 17min 2s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 15/39
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 30/69[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 14/42[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 14/97
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 9/76
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 31/82
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-27 12:09:38. Total running time: 17min 32s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 36/45[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 12/38
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m50s[0m 91ms/step - accuracy: 0.2812 - loss: 2.1255 - val_accuracy: 0.3698 - val_loss: 1.6799
[36m(train_cnn_ray_tune pid=571081)[0m Epoch 16/39
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:54[0m 209ms/step - accuracy: 0.2188 - loss: 2.2935
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 37/45
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 187ms/step - accuracy: 0.2500 - loss: 1.9022
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 46ms/step - accuracy: 0.2822 - loss: 1.8668 - val_accuracy: 0.3413 - val_loss: 1.5834[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 32/69[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[1m 35/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 39ms/step - accuracy: 0.1835 - loss: 2.3641[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:19[0m 145ms/step - accuracy: 0.1875 - loss: 2.3991
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 280ms/step - accuracy: 0.1484 - loss: 2.4018
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-27 12:10:08. Total running time: 18min 2s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m136/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m45s[0m 112ms/step - accuracy: 0.2963 - loss: 1.9623
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m143/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m44s[0m 111ms/step - accuracy: 0.2969 - loss: 1.9610[32m [repeated 102x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m294/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m34s[0m 138ms/step - accuracy: 0.2609 - loss: 2.0444
[1m295/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m34s[0m 138ms/step - accuracy: 0.2609 - loss: 2.0444
[36m(train_cnn_ray_tune pid=571061)[0m Epoch 27/31
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m Epoch 13/49
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 271ms/step - accuracy: 0.4531 - loss: 1.4080
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m221/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m24s[0m 76ms/step - accuracy: 0.2787 - loss: 2.0983
[1m222/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m24s[0m 76ms/step - accuracy: 0.2787 - loss: 2.0982[32m [repeated 41x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m57s[0m 208ms/step - accuracy: 0.4502 - loss: 1.4584 - val_accuracy: 0.4549 - val_loss: 1.3879
[36m(train_cnn_ray_tune pid=571055)[0m Epoch 15/42
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m56s[0m 102ms/step - accuracy: 0.3183 - loss: 1.7190 - val_accuracy: 0.3411 - val_loss: 1.5364
[36m(train_cnn_ray_tune pid=571077)[0m Epoch 15/97
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 267ms/step - accuracy: 0.4375 - loss: 1.5032
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:32[0m 169ms/step - accuracy: 0.3750 - loss: 1.7960
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 38/45[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 33/82[32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 33/69
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 28/31
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 300ms/step - accuracy: 0.2109 - loss: 2.2655
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-27 12:10:38. Total running time: 18min 32s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 39/45
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 174ms/step - accuracy: 0.3281 - loss: 1.6941
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:39:49[0m 18s/step - accuracy: 0.1250 - loss: 2.4793
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 69/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m12s[0m 63ms/step - accuracy: 0.2875 - loss: 1.8247
[1m 70/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m12s[0m 63ms/step - accuracy: 0.2874 - loss: 1.8249[32m [repeated 46x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m48s[0m 88ms/step - accuracy: 0.2825 - loss: 2.0910 - val_accuracy: 0.3660 - val_loss: 1.6479
[36m(train_cnn_ray_tune pid=571081)[0m Epoch 17/39
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m535/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - accuracy: 0.2986 - loss: 1.8431[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 149ms/step - accuracy: 0.3125 - loss: 1.8464
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m365/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m16s[0m 92ms/step - accuracy: 0.3355 - loss: 1.7083
[1m366/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m16s[0m 92ms/step - accuracy: 0.3355 - loss: 1.7083[32m [repeated 66x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 46ms/step - accuracy: 0.2985 - loss: 1.8430 - val_accuracy: 0.3510 - val_loss: 1.5709
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 34/82
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m83s[0m 152ms/step - accuracy: 0.2623 - loss: 2.0417 - val_accuracy: 0.2632 - val_loss: 1.8628
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 10/76
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:05[0m 230ms/step - accuracy: 0.3750 - loss: 1.8812
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 144ms/step - accuracy: 0.3594 - loss: 1.9249
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 137ms/step - accuracy: 0.3333 - loss: 1.9943
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m193/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.2839 - loss: 1.8368[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  4/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 134ms/step - accuracy: 0.3066 - loss: 2.0433
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m197/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.2839 - loss: 1.8372
[1m198/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.2838 - loss: 1.8372[32m [repeated 23x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  6/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 133ms/step - accuracy: 0.2860 - loss: 2.0596
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 13/38
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 568
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 40/45[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 34/69[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-27 12:11:08. Total running time: 19min 2s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 16/97
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 41/45
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m Epoch 30/31
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 307ms/step - accuracy: 0.2109 - loss: 2.4154
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 45ms/step - accuracy: 0.1930 - loss: 2.2932 - val_accuracy: 0.2654 - val_loss: 1.8508
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 35/69
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 149ms/step - accuracy: 0.1562 - loss: 2.2631
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 143ms/step - accuracy: 0.2500 - loss: 1.9757
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 11/70[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-27 12:11:38. Total running time: 19min 32s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 42/45
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 10/64
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 10/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 306ms/step - accuracy: 0.3182 - loss: 1.7566
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 14/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:17[0m 297ms/step - accuracy: 0.3187 - loss: 1.7622
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 17/42[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 332ms/step - accuracy: 0.5000 - loss: 1.1713[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 18/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:17[0m 301ms/step - accuracy: 0.3201 - loss: 1.7643[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 13/137[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 193ms/step - accuracy: 0.2053 - loss: 2.2966[32m [repeated 102x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 46ms/step - accuracy: 0.1928 - loss: 2.2853 - val_accuracy: 0.2660 - val_loss: 1.8434
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m Epoch 15/49[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-27 12:12:08. Total running time: 20min 2s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m Epoch 17/97
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m103/274[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m28s[0m 165ms/step - accuracy: 0.4648 - loss: 1.4023[32m [repeated 97x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[1m382/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m6s[0m 39ms/step - accuracy: 0.1988 - loss: 2.2635
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 32/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 82ms/step - accuracy: 0.2962 - loss: 1.7197
[1m 33/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 82ms/step - accuracy: 0.2968 - loss: 1.7198[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 147ms/step - accuracy: 0.2711 - loss: 1.9695 - val_accuracy: 0.2854 - val_loss: 1.7053
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 11/76
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:06[0m 232ms/step - accuracy: 0.2812 - loss: 1.8770
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m202/274[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 64ms/step - accuracy: 0.2825 - loss: 1.8375
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 181ms/step - accuracy: 0.2044 - loss: 2.3021[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 16/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 130ms/step - accuracy: 0.2638 - loss: 1.9808[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m135/274[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m22s[0m 163ms/step - accuracy: 0.4636 - loss: 1.4059[32m [repeated 97x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 20/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 130ms/step - accuracy: 0.2661 - loss: 1.9798
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 86/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m39s[0m 86ms/step - accuracy: 0.3126 - loss: 1.7150
[1m 87/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m39s[0m 86ms/step - accuracy: 0.3128 - loss: 1.7149[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 38/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 125ms/step - accuracy: 0.2668 - loss: 1.9755
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m270/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 63ms/step - accuracy: 0.2830 - loss: 1.8378[32m [repeated 76x across cluster][0m
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m271/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 63ms/step - accuracy: 0.2830 - loss: 1.8378
[1m273/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 63ms/step - accuracy: 0.2830 - loss: 1.8377[32m [repeated 47x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m48s[0m 88ms/step - accuracy: 0.3023 - loss: 2.0205 - val_accuracy: 0.3767 - val_loss: 1.6083
[36m(train_cnn_ray_tune pid=571081)[0m Epoch 19/39
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:19[0m 145ms/step - accuracy: 0.3125 - loss: 1.7362
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m27s[0m 199ms/step - accuracy: 0.2045 - loss: 2.3011 - val_accuracy: 0.2281 - val_loss: 2.0339
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 57/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 121ms/step - accuracy: 0.2650 - loss: 1.9771
[1m 58/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m58s[0m 120ms/step - accuracy: 0.2650 - loss: 1.9768
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  6/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 62ms/step - accuracy: 0.3109 - loss: 2.0090[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34s[0m 64ms/step - accuracy: 0.3203 - loss: 1.8435  [32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 60/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m58s[0m 120ms/step - accuracy: 0.2651 - loss: 1.9760[32m [repeated 112x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:05[0m 885ms/step
[1m 3/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 36ms/step   
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 4/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 42ms/step
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 37ms/step
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 8/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 35ms/step
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 33ms/step
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m14/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 31ms/step
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 31ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m38/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 33ms/step
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m42/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 33ms/step
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m44/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 33ms/step
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 33ms/step
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m53/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 33ms/step
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 33ms/step
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m66/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 33ms/step
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 41ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 138ms/step
[1m  3/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step  
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 45ms/step - accuracy: 0.2929 - loss: 1.8292 - val_accuracy: 0.3500 - val_loss: 1.5635[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 37/82[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  5/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 31ms/step
[1m  7/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 32ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 125ms/step - accuracy: 0.5000 - loss: 1.4798[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m82s[0m 119ms/step - accuracy: 0.3078 - loss: 1.8888 - val_accuracy: 0.3717 - val_loss: 1.5222
[36m(train_cnn_ray_tune pid=571061)[0m 
[1m  8/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 35ms/step
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 12/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 38ms/step
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 14/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 38ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 18/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 37ms/step
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 24/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 38ms/step
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 41/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 36ms/step
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m - loss: 2.0325
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
[1m 52/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 36ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[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=571061)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571061)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:12:30. Total running time: 20min 24s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1220.97 │
│ time_total_s                 1220.97 │
│ training_iteration                 1 │
│ val_accuracy                 0.22806 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:12:30. Total running time: 20min 24s
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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Trial status: 11 RUNNING | 9 TERMINATED
Current time: 2025-10-27 12:12:38. Total running time: 20min 32s
Logical resource usage: 11.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m Epoch 45/45
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 38/82[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 12/70
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m55s[0m 199ms/step - accuracy: 0.4698 - loss: 1.3667 - val_accuracy: 0.4607 - val_loss: 1.3477
[36m(train_cnn_ray_tune pid=571092)[0m Epoch 16/49
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:24[0m 311ms/step - accuracy: 0.4688 - loss: 1.4838
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[1m399/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m14s[0m 101ms/step - accuracy: 0.3244 - loss: 1.8309[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 1s/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 4/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[1m 7/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step
[1m13/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[1m18/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 78ms/step - accuracy: 0.2891 - loss: 1.9028  
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m21/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m23/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m26/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step
[1m29/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m  5/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 80ms/step - accuracy: 0.3093 - loss: 1.8171
[1m  6/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 77ms/step - accuracy: 0.3133 - loss: 1.8111
[36m(train_cnn_ray_tune pid=571077)[0m Epoch 18/97
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m31/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 24ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m34/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 24ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m525/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - accuracy: 0.2020 - loss: 2.2451
[1m527/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - accuracy: 0.2020 - loss: 2.2451
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m38/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m  8/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 80ms/step - accuracy: 0.3120 - loss: 1.7969
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m40/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m42/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 24ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 23ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 23ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 24ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 24ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m521/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - accuracy: 0.2982 - loss: 1.8087
[1m523/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - accuracy: 0.2982 - loss: 1.8087[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m528/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - accuracy: 0.2983 - loss: 1.8087[32m [repeated 63x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 38ms/step
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 38ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m452/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m9s[0m 100ms/step - accuracy: 0.3246 - loss: 1.8314[32m [repeated 38x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 79ms/step
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m  6/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 12/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 18/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step
[1m 20/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m54s[0m 99ms/step - accuracy: 0.3258 - loss: 1.6932 - val_accuracy: 0.3551 - val_loss: 1.5525
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m47s[0m 85ms/step - accuracy: 0.3119 - loss: 1.9976 - val_accuracy: 0.3743 - val_loss: 1.5996
[36m(train_cnn_ray_tune pid=571081)[0m Epoch 20/39
[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 23/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 28/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:27[0m 160ms/step - accuracy: 0.3438 - loss: 1.9201
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 33/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 43/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 48/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m 54/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
[1m111/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 23ms/step
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Trial status: 11 RUNNING | 9 TERMINATED
Current time: 2025-10-27 12:13:08. Total running time: 21min 2s
Logical resource usage: 11.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              5   adam            relu                                   64                 32                  3          0.000219563         45                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[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=571070)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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[36m(train_cnn_ray_tune pid=571070)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:13:09. Total running time: 21min 3s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1260.64 │
│ time_total_s                 1260.64 │
│ training_iteration                 1 │
│ val_accuracy                 0.34051 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:13:09. Total running time: 21min 3s
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 39/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 11/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:02[0m 238ms/step - accuracy: 0.3116 - loss: 1.6965
[36m(train_cnn_ray_tune pid=571043)[0m Epoch 11/64
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m60s[0m 110ms/step - accuracy: 0.3248 - loss: 1.8320 - val_accuracy: 0.4012 - val_loss: 1.4801
[36m(train_cnn_ray_tune pid=571049)[0m Epoch 15/38
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m Epoch 19/42
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m76s[0m 138ms/step - accuracy: 0.2782 - loss: 1.9239 - val_accuracy: 0.2850 - val_loss: 1.7133
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 40/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial status: 10 RUNNING | 10 TERMINATED
Current time: 2025-10-27 12:13:38. Total running time: 21min 32s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              5   rmsprop         tanh                                   64                256                  5          0.00102495          49                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 40/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 41/82[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m  3/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 37ms/step  
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 39/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m5s[0m 45ms/step
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 45/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 46ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 45ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 76/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 42ms/step
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 80/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 42ms/step
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 86/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 13/70
[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 92/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 42ms/step
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 96/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 43ms/step
[1m 99/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m  4/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:01[0m 227ms/step - accuracy: 0.3753 - loss: 1.7521
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m  5/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m58s[0m 219ms/step - accuracy: 0.3684 - loss: 1.7509 
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m320/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m7s[0m 33ms/step - accuracy: 0.1951 - loss: 2.2384
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m105/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571092)[0m 
[1m107/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 42ms/step
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m110/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 42ms/step
[1m112/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m114/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 42ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
[1m116/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 42ms/step
[1m117/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 43ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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[36m(train_cnn_ray_tune pid=571092)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:14:02. Total running time: 21min 56s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1313.33 │
│ time_total_s                 1313.33 │
│ training_iteration                 1 │
│ val_accuracy                  0.4502 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:14:02. Total running time: 21min 56s
[36m(train_cnn_ray_tune pid=571092)[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=571092)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial status: 9 RUNNING | 11 TERMINATED
Current time: 2025-10-27 12:14:08. Total running time: 22min 2s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 41/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 42/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 16/38
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 12/64
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 42/69[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 116ms/step - accuracy: 0.2500 - loss: 2.3501
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 43/82
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 13/76
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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Trial status: 9 RUNNING | 11 TERMINATED
Current time: 2025-10-27 12:14:38. Total running time: 22min 32s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              8   rmsprop         relu                                   32                 64                  5          0.00211867          97                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    RUNNING              4   adam            tanh                                   64                256                  5          0.000389976         42                                              │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 30ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
[1m17/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
[1m21/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m28/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m398/547[0m [32m━━━━━━━━━━━�
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 30ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m52/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 30ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 30ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 30ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m69/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 30ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 30ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m239/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m4s[0m 121ms/step - accuracy: 0.4862 - loss: 1.3448[32m [repeated 113x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 30ms/step
[36m(train_cnn_ray_tune pid=571078)[0m �━━[0m[37m━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.2172 - loss: 2.1968
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 80/274[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m39s[0m 204ms/step - accuracy: 0.3443 - loss: 1.6846[32m [repeated 74x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 47ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m  9/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 31ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 19/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m273/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m16s[0m 59ms/step - accuracy: 0.3295 - loss: 1.9000
[1m274/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m16s[0m 59ms/step - accuracy: 0.3295 - loss: 1.9001[32m [repeated 41x across cluster][0m
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 46/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 31ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m 59/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 31ms/step
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 30ms/step
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m106/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 30ms/step
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m131/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 30ms/step
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571077)[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=571077)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m140/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 30ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m149/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 30ms/step
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m153/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 30ms/step
[1m156/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571077)[0m 
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[36m(train_cnn_ray_tune pid=571077)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 30ms/step

Trial trial_fa758 finished iteration 1 at 2025-10-27 12:14:48. Total running time: 22min 42s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1359.47 │
│ time_total_s                 1359.47 │
│ training_iteration                 1 │
│ val_accuracy                 0.33123 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:14:48. Total running time: 22min 42s
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 35ms/step - accuracy: 0.2168 - loss: 2.1965 - val_accuracy: 0.2757 - val_loss: 1.7789
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 43/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m 23/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 28ms/step - accuracy: 0.2485 - loss: 2.1332
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m377/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m9s[0m 59ms/step - accuracy: 0.3291 - loss: 1.9034 
[1m378/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m9s[0m 59ms/step - accuracy: 0.3291 - loss: 1.9034
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 3/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step  
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m11/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 30ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m20/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m24/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m32/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 28ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 28ms/step
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 35ms/step - accuracy: 0.3174 - loss: 1.7673 - val_accuracy: 0.3684 - val_loss: 1.5259
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 44/82
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m 58/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 25ms/step - accuracy: 0.3087 - loss: 1.7766
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 10/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 14/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 19/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 23/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 28ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 28/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step
[1m 31/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m157/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m9s[0m 26ms/step - accuracy: 0.2202 - loss: 2.1801 
[1m159/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m9s[0m 26ms/step - accuracy: 0.2200 - loss: 2.1804
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 33/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 37/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 41/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 44/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m57s[0m 207ms/step - accuracy: 0.3270 - loss: 1.7508 - val_accuracy: 0.3518 - val_loss: 1.5323
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 46/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step
[36m(train_cnn_ray_tune pid=571085)[0m Epoch 14/70
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step
[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 53/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m172/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m37s[0m 100ms/step - accuracy: 0.3041 - loss: 1.8264
[1m173/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m37s[0m 100ms/step - accuracy: 0.3041 - loss: 1.8265[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 55/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step
[1m 58/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m  2/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 178ms/step - accuracy: 0.3555 - loss: 1.7644 
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 65/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m286/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m6s[0m 26ms/step - accuracy: 0.2139 - loss: 2.1899
[1m289/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m6s[0m 26ms/step - accuracy: 0.2139 - loss: 2.1901[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 67/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 28ms/step
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 71/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 29ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 76/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 28ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 78/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 81/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 83/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 85/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 29ms/step
[1m 87/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m511/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 56ms/step - accuracy: 0.3286 - loss: 1.9071
[1m512/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 56ms/step - accuracy: 0.3286 - loss: 1.9071
[1m513/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 56ms/step - accuracy: 0.3286 - loss: 1.9071
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 89/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 29ms/step
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[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 94/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 29ms/step
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m206/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m33s[0m 98ms/step - accuracy: 0.3038 - loss: 1.8277[32m [repeated 41x across cluster][0m
[36m(train_cnn_ray_tune pid=571055)[0m 
[1m 98/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 29ms/step
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[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=571055)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571055)[0m 
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[36m(train_cnn_ray_tune pid=571055)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:14:58. Total running time: 22min 52s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1369.69 │
│ time_total_s                 1369.69 │
│ training_iteration                 1 │
│ val_accuracy                 0.45731 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:14:58. Total running time: 22min 52s
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 23/39
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 44/69[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:15:08. Total running time: 23min 2s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 45/82
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 13/64
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 46/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 14/76[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 47/82[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 47/69
Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:15:38. Total running time: 23min 32s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m183/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m8s[0m 24ms/step - accuracy: 0.2055 - loss: 2.1936[32m [repeated 36x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 129ms/step - accuracy: 0.3125 - loss: 1.5260
[1m  4/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 19ms/step - accuracy: 0.3704 - loss: 1.5717  
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 25ms/step - accuracy: 0.3228 - loss: 1.7519 - val_accuracy: 0.3763 - val_loss: 1.4999
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 48/82
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 25/39
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 48/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 49/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 15/76
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:16:09. Total running time: 24min 2s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 16/70[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 18/38[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 50/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 51/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:02[0m 114ms/step - accuracy: 0.2500 - loss: 1.7360
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:16:39. Total running time: 24min 32s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 51/69
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 94ms/step - accuracy: 0.2188 - loss: 2.0949
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m447/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m8s[0m 83ms/step - accuracy: 0.3237 - loss: 1.7490[32m [repeated 63x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m30s[0m 55ms/step - accuracy: 0.3377 - loss: 1.8409 - val_accuracy: 0.4055 - val_loss: 1.5194
[36m(train_cnn_ray_tune pid=571081)[0m Epoch 27/39
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 29ms/step - accuracy: 0.3278 - loss: 1.7341 - val_accuracy: 0.3787 - val_loss: 1.4856
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 52/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 105ms/step - accuracy: 0.4062 - loss: 1.7551
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 15/64
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m154/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m17s[0m 45ms/step - accuracy: 0.3435 - loss: 1.8125
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[1m 45/274[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m32s[0m 141ms/step - accuracy: 0.3705 - loss: 1.6107
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 17/70
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 16/76[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 53/82
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:17:09. Total running time: 25min 3s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 53/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 28/39
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 29ms/step - accuracy: 0.3325 - loss: 1.7200 - val_accuracy: 0.3743 - val_loss: 1.5071
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 54/82
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 54/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 55/82
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 18/70[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:17:39. Total running time: 25min 33s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 55/69[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 56/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 56/69
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[1m460/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m4s[0m 54ms/step - accuracy: 0.3771 - loss: 1.6816[32m [repeated 42x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 27ms/step - accuracy: 0.3440 - loss: 1.6924 - val_accuracy: 0.3751 - val_loss: 1.5048
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 57/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:06[0m 121ms/step - accuracy: 0.4375 - loss: 1.6287
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 21/38
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:18:09. Total running time: 26min 3s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 28ms/step - accuracy: 0.3433 - loss: 1.7108 - val_accuracy: 0.3785 - val_loss: 1.5074
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 58/82
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 30/39[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 17/64
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 59/82
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 18/76[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:18:39. Total running time: 26min 33s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 59/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 31/39[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 20/70
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 61/82[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:19:09. Total running time: 27min 3s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571043)[0m Epoch 18/64
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 61/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 148ms/step - accuracy: 0.4688 - loss: 1.5461
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m49s[0m 89ms/step - accuracy: 0.3670 - loss: 1.6346 - val_accuracy: 0.3545 - val_loss: 1.5373[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 19/76[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 23/38
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 62/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 63/82
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:19:39. Total running time: 27min 33s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m Epoch 21/70
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 64/82[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 19/64
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 65/82[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 20/76
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:20:09. Total running time: 28min 3s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[1m195/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m21s[0m 61ms/step - accuracy: 0.4018 - loss: 1.5694[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m Epoch 34/39
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 129ms/step - accuracy: 0.3125 - loss: 2.1939
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 65/69
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 117ms/step - accuracy: 0.1875 - loss: 1.9409
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m138/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m9s[0m 24ms/step - accuracy: 0.2533 - loss: 2.0329 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m170/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 24ms/step - accuracy: 0.2513 - loss: 2.0339
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 66/82
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m241/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.2498 - loss: 2.0336[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m150/274[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m19s[0m 157ms/step - accuracy: 0.4114 - loss: 1.5234[32m [repeated 32x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47:52[0m 5s/step - accuracy: 0.2500 - loss: 1.6228
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m118/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.3531 - loss: 1.6627 
[1m121/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.3533 - loss: 1.6628
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m44s[0m 159ms/step - accuracy: 0.3483 - loss: 1.6947 - val_accuracy: 0.3502 - val_loss: 1.5124
[36m(train_cnn_ray_tune pid=571085)[0m Epoch 22/70
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m183/274[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m14s[0m 156ms/step - accuracy: 0.4105 - loss: 1.5242[32m [repeated 33x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 257ms/step - accuracy: 0.4688 - loss: 1.3585
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m Epoch 66/69
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m Epoch 67/82
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:20:39. Total running time: 28min 33s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 35/39[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 20/64[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 28ms/step - accuracy: 0.3639 - loss: 1.6537 - val_accuracy: 0.3919 - val_loss: 1.4661
[36m(train_cnn_ray_tune pid=571080)[0m Epoch 68/82
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m58s[0m 106ms/step - accuracy: 0.3125 - loss: 1.8193
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m221/274[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m7s[0m 139ms/step - accuracy: 0.3465 - loss: 1.6682[32m [repeated 74x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 79ms/step - accuracy: 0.4922 - loss: 1.4362  
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m49s[0m 89ms/step - accuracy: 0.3964 - loss: 1.5590 - val_accuracy: 0.4016 - val_loss: 1.4606
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 21/76
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:27[0m 160ms/step - accuracy: 0.5312 - loss: 1.3457
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 28ms/step - accuracy: 0.2497 - loss: 2.0189 - val_accuracy: 0.2982 - val_loss: 1.6488
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 68/69
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 36/39
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-27 12:21:09. Total running time: 29min 3s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69                                              │
│ trial_fa758    RUNNING              3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[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=571080)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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[36m(train_cnn_ray_tune pid=571080)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:21:12. Total running time: 29min 6s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1743.49 │
│ time_total_s                 1743.49 │
│ training_iteration                 1 │
│ val_accuracy                 0.39249 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:21:12. Total running time: 29min 6s
[36m(train_cnn_ray_tune pid=571080)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 27ms/step - accuracy: 0.3635 - loss: 1.6442 - val_accuracy: 0.3925 - val_loss: 1.4706
[36m(train_cnn_ray_tune pid=571085)[0m Epoch 23/70
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:02[0m 229ms/step - accuracy: 0.3594 - loss: 1.6497
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m130/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - accuracy: 0.3882 - loss: 1.6752
[1m131/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - accuracy: 0.3882 - loss: 1.6753[32m [repeated 42x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[1m544/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 23ms/step - accuracy: 0.2469 - loss: 2.0154[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 27ms/step - accuracy: 0.2469 - loss: 2.0154 - val_accuracy: 0.3032 - val_loss: 1.6453
[36m(train_cnn_ray_tune pid=571078)[0m Epoch 69/69
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 96ms/step - accuracy: 0.3125 - loss: 1.7144
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m  4/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 18ms/step - accuracy: 0.2956 - loss: 1.8651 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 26/38
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 144ms/step - accuracy: 0.2812 - loss: 1.9810
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 543ms/step
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 21/64
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m 15/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m 27/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step
[1m 32/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[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=571078)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m 47/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m 85/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[1m110/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
[1m115/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m294/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m14s[0m 58ms/step - accuracy: 0.4219 - loss: 1.5402
[1m296/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m14s[0m 58ms/step - accuracy: 0.4219 - loss: 1.5402[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m142/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 10ms/step
[1m146/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
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[36m(train_cnn_ray_tune pid=571078)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step

Trial trial_fa758 finished iteration 1 at 2025-10-27 12:21:34. Total running time: 29min 28s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1765.21 │
│ time_total_s                 1765.21 │
│ training_iteration                 1 │
│ val_accuracy                 0.30435 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:21:34. Total running time: 29min 28s
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 149ms/step - accuracy: 0.3750 - loss: 1.8667
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m504/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m3s[0m 77ms/step - accuracy: 0.4135 - loss: 1.5178[32m [repeated 31x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 62/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 45ms/step - accuracy: 0.3911 - loss: 1.6913[32m [repeated 60x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m Epoch 37/39
[36m(train_cnn_ray_tune pid=571081)[0m 
[1m 95/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m19s[0m 43ms/step - accuracy: 0.3941 - loss: 1.6886
[1m 97/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m19s[0m 43ms/step - accuracy: 0.3943 - loss: 1.6883[32m [repeated 38x across cluster][0m

Trial status: 5 RUNNING | 15 TERMINATED
Current time: 2025-10-27 12:21:39. Total running time: 29min 33s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 22/76
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m112/274[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m20s[0m 127ms/step - accuracy: 0.4020 - loss: 1.5296[32m [repeated 41x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 47/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 61ms/step - accuracy: 0.4470 - loss: 1.4272[32m [repeated 31x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m39s[0m 141ms/step - accuracy: 0.3433 - loss: 1.6745 - val_accuracy: 0.3512 - val_loss: 1.5195
[36m(train_cnn_ray_tune pid=571085)[0m Epoch 24/70
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 83/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m27s[0m 60ms/step - accuracy: 0.4426 - loss: 1.4385
[1m 84/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m27s[0m 60ms/step - accuracy: 0.4425 - loss: 1.4387[32m [repeated 32x across cluster][0m
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 174ms/step - accuracy: 0.4375 - loss: 1.4031
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m33s[0m 60ms/step - accuracy: 0.4208 - loss: 1.5357 - val_accuracy: 0.4494 - val_loss: 1.3598
[36m(train_cnn_ray_tune pid=571049)[0m Epoch 27/38
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 126ms/step - accuracy: 0.5625 - loss: 1.1402
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m439/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m3s[0m 36ms/step - accuracy: 0.3939 - loss: 1.6859
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m 18/274[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 105ms/step - accuracy: 0.3589 - loss: 1.6288
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m 85/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m24s[0m 53ms/step - accuracy: 0.4367 - loss: 1.4905
[1m 86/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m24s[0m 54ms/step - accuracy: 0.4367 - loss: 1.4907[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 38/39
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial status: 5 RUNNING | 15 TERMINATED
Current time: 2025-10-27 12:22:09. Total running time: 30min 3s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 22/64
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m Epoch 39/39
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m40s[0m 73ms/step - accuracy: 0.4275 - loss: 1.4746 - val_accuracy: 0.4018 - val_loss: 1.4468[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 23/76[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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Trial status: 5 RUNNING | 15 TERMINATED
Current time: 2025-10-27 12:22:39. Total running time: 30min 33s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39                                              │
│ trial_fa758    RUNNING              7   rmsprop         tanh                                   64                256                  5          0.00312316          70                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[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=571081)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
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[36m(train_cnn_ray_tune pid=571081)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 12ms/step

Trial trial_fa758 finished iteration 1 at 2025-10-27 12:22:48. Total running time: 30min 42s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1838.87 │
│ time_total_s                 1838.87 │
│ training_iteration                 1 │
│ val_accuracy                 0.42648 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:22:48. Total running time: 30min 42s
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m37s[0m 136ms/step - accuracy: 0.4158 - loss: 1.5027 - val_accuracy: 0.4358 - val_loss: 1.4124
[36m(train_cnn_ray_tune pid=571043)[0m Epoch 23/64
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 149ms/step - accuracy: 0.3281 - loss: 1.7229
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 35/274[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 103ms/step - accuracy: 0.4176 - loss: 1.4656[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 29/38
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:04[0m 118ms/step - accuracy: 0.4375 - loss: 1.3988
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 56/274[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m22s[0m 102ms/step - accuracy: 0.4175 - loss: 1.4690
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:07[0m 909ms/step
[36m(train_cnn_ray_tune pid=571085)[0m 
[1m 4/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step   
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m 9/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[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=571085)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m109/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 24/76
[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
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[36m(train_cnn_ray_tune pid=571085)[0m 
[1m158/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 19ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 19ms/step

Trial trial_fa758 finished iteration 1 at 2025-10-27 12:23:01. Total running time: 30min 55s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1852.51 │
│ time_total_s                 1852.51 │
│ training_iteration                 1 │
│ val_accuracy                 0.36146 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:23:01. Total running time: 30min 55s
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial status: 3 RUNNING | 17 TERMINATED
Current time: 2025-10-27 12:23:10. Total running time: 31min 3s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39        1           1838.87          0.426482 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                256                  5          0.00312316          70        1           1852.51          0.361462 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m243/274[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 85ms/step - accuracy: 0.4220 - loss: 1.4738[32m [repeated 51x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m248/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 85ms/step - accuracy: 0.4222 - loss: 1.4738
[1m249/274[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 85ms/step - accuracy: 0.4222 - loss: 1.4737[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 38ms/step - accuracy: 0.4453 - loss: 1.4599 - val_accuracy: 0.4540 - val_loss: 1.3591
[36m(train_cnn_ray_tune pid=571049)[0m Epoch 30/38
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 24/64
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m  1/274[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 139ms/step - accuracy: 0.3750 - loss: 1.4820
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m487/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 30ms/step - accuracy: 0.4520 - loss: 1.4457
[1m490/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 30ms/step - accuracy: 0.4520 - loss: 1.4457[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 46ms/step - accuracy: 0.4351 - loss: 1.4535 - val_accuracy: 0.4346 - val_loss: 1.4001
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 25/76
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m55s[0m 101ms/step - accuracy: 0.5312 - loss: 1.2723
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 67/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 39ms/step - accuracy: 0.4458 - loss: 1.4022[32m [repeated 26x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m209/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m4s[0m 75ms/step - accuracy: 0.4290 - loss: 1.4628[32m [repeated 38x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m210/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m4s[0m 75ms/step - accuracy: 0.4290 - loss: 1.4628
[1m211/274[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m4s[0m 75ms/step - accuracy: 0.4290 - loss: 1.4627[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 34ms/step - accuracy: 0.4516 - loss: 1.4459 - val_accuracy: 0.4684 - val_loss: 1.3650
[36m(train_cnn_ray_tune pid=571049)[0m Epoch 31/38
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 85ms/step - accuracy: 0.5000 - loss: 1.3682
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m129/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m13s[0m 32ms/step - accuracy: 0.4487 - loss: 1.4336[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m177/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m11s[0m 32ms/step - accuracy: 0.4465 - loss: 1.4359
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m273/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 76ms/step - accuracy: 0.4301 - loss: 1.4610[32m [repeated 36x across cluster][0m
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m267/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 76ms/step - accuracy: 0.4300 - loss: 1.4611
[1m268/274[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 76ms/step - accuracy: 0.4300 - loss: 1.4610[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m231/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.4457 - loss: 1.4374 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m274/274[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m23s[0m 84ms/step - accuracy: 0.4301 - loss: 1.4610 - val_accuracy: 0.4294 - val_loss: 1.4434
[36m(train_cnn_ray_tune pid=571043)[0m Epoch 25/64
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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Trial status: 3 RUNNING | 17 TERMINATED
Current time: 2025-10-27 12:23:40. Total running time: 31min 33s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39        1           1838.87          0.426482 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                256                  5          0.00312316          70        1           1852.51          0.361462 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 35ms/step - accuracy: 0.4488 - loss: 1.4394 - val_accuracy: 0.4619 - val_loss: 1.3579
[36m(train_cnn_ray_tune pid=571049)[0m Epoch 32/38
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 26/76
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 100ms/step - accuracy: 0.3750 - loss: 1.7390
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 26/64
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 33/38
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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Trial status: 3 RUNNING | 17 TERMINATED
Current time: 2025-10-27 12:24:10. Total running time: 32min 4s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39        1           1838.87          0.426482 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                256                  5          0.00312316          70        1           1852.51          0.361462 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 45ms/step - accuracy: 0.4381 - loss: 1.4106 - val_accuracy: 0.4383 - val_loss: 1.3877
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 27/76
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m Epoch 27/64
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 34/38
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 28/76
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial status: 3 RUNNING | 17 TERMINATED
Current time: 2025-10-27 12:24:40. Total running time: 32min 34s
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_fa758    RUNNING              8   adam            tanh                                   64                256                  5          0.000595066         64                                              │
│ trial_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39        1           1838.87          0.426482 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                256                  5          0.00312316          70        1           1852.51          0.361462 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 35/38
[36m(train_cnn_ray_tune pid=571049)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 75ms/step - accuracy: 0.4688 - loss: 1.2258
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step
[36m(train_cnn_ray_tune pid=571043)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 25ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 78/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m 82/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 17ms/step
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
[1m 94/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 17ms/step
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[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=571043)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571043)[0m 
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[36m(train_cnn_ray_tune pid=571043)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:24:52. Total running time: 32min 45s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1963.04 │
│ time_total_s                 1963.04 │
│ training_iteration                 1 │
│ val_accuracy                 0.44269 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:24:52. Total running time: 32min 45s
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 36/38
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 29/76
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m Epoch 37/38
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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Trial status: 18 TERMINATED | 2 RUNNING
Current time: 2025-10-27 12:25:10. Total running time: 33min 4s
Logical resource usage: 2.0/20 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_fa758    RUNNING              4   rmsprop         relu                                   32                256                  5          4.07199e-05         38                                              │
│ trial_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   adam            tanh                                   64                256                  5          0.000595066         64        1           1963.04          0.442688 │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39        1           1838.87          0.426482 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                256                  5          0.00312316          70        1           1852.51          0.361462 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m Epoch 30/76
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[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=571049)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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[36m(train_cnn_ray_tune pid=571049)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:25:23. Total running time: 33min 17s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             1994.24 │
│ time_total_s                 1994.24 │
│ training_iteration                 1 │
│ val_accuracy                 0.46225 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:25:23. Total running time: 33min 17s
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 31/76
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[1m332/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 15ms/step - accuracy: 0.4765 - loss: 1.3266[32m [repeated 32x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 17ms/step - accuracy: 0.4735 - loss: 1.3306 - val_accuracy: 0.4447 - val_loss: 1.4508
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 32/76
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 34ms/step - accuracy: 0.4375 - loss: 1.2254
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  5/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.4511 - loss: 1.2713 
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[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 61/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 15ms/step - accuracy: 0.4783 - loss: 1.3009[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m 65/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 15ms/step - accuracy: 0.4790 - loss: 1.3012
[1m 69/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 15ms/step - accuracy: 0.4797 - loss: 1.3012[32m [repeated 26x across cluster][0m

Trial status: 19 TERMINATED | 1 RUNNING
Current time: 2025-10-27 12:25:40. Total running time: 33min 34s
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_fa758    RUNNING              6   rmsprop         relu                                   32                256                  5          7.32936e-05         76                                              │
│ trial_fa758    TERMINATED           8   adam            tanh                                   64                256                  5          0.000595066         64        1           1963.04          0.442688 │
│ trial_fa758    TERMINATED           4   rmsprop         relu                                   32                256                  5          4.07199e-05         38        1           1994.24          0.462253 │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39        1           1838.87          0.426482 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                256                  5          0.00312316          70        1           1852.51          0.361462 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m385/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 15ms/step - accuracy: 0.4756 - loss: 1.3123[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m397/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 15ms/step - accuracy: 0.4755 - loss: 1.3127
[1m401/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 15ms/step - accuracy: 0.4754 - loss: 1.3128[32m [repeated 33x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 17ms/step - accuracy: 0.4745 - loss: 1.3162 - val_accuracy: 0.4429 - val_loss: 1.4235
[36m(train_cnn_ray_tune pid=571067)[0m Epoch 33/76
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 32ms/step - accuracy: 0.5000 - loss: 1.1877
2025-10-27 12:25:55,410	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_ESANN_acc_gyr_17_classes/ESANN_hyperparameters_tuning' in 0.0071s.
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m  5/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 16ms/step - accuracy: 0.4954 - loss: 1.3079 
[36m(train_cnn_ray_tune pid=571067)[0m 
[1m136/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 16ms/step - accuracy: 0.4666 - loss: 1.3572[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[0m 
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Trial trial_fa758 finished iteration 1 at 2025-10-27 12:25:55. Total running time: 33min 49s
╭──────────────────────────────────────╮
│ Trial trial_fa758 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             2026.24 │
│ time_total_s                 2026.24 │
│ training_iteration                 1 │
│ val_accuracy                 0.44368 │
╰──────────────────────────────────────╯

Trial trial_fa758 completed after 1 iterations at 2025-10-27 12:25:55. Total running time: 33min 49s

Trial status: 20 TERMINATED
Current time: 2025-10-27 12:25:55. Total running time: 33min 49s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
[36m(train_cnn_ray_tune pid=571067)[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=571067)[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 00:00:1761564355.558460  569414 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12394 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_fa758    TERMINATED           8   adam            tanh                                   64                256                  5          0.000595066         64        1           1963.04          0.442688 │
│ trial_fa758    TERMINATED           4   rmsprop         relu                                   32                256                  5          4.07199e-05         38        1           1994.24          0.462253 │
│ trial_fa758    TERMINATED           8   rmsprop         relu                                   32                 64                  5          0.00211867          97        1           1359.47          0.331225 │
│ trial_fa758    TERMINATED           6   adam            relu                                  128                256                  3          1.69169e-05         82        1            788.259         0.170553 │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                 32                  3          0.00903085          67        1            405.652         0.135375 │
│ trial_fa758    TERMINATED           3   rmsprop         tanh                                   32                256                  3          3.42479e-05         39        1           1838.87          0.426482 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  3          2.31015e-05         69        1           1765.21          0.304348 │
│ trial_fa758    TERMINATED           3   rmsprop         relu                                   32                 32                  5          7.31962e-05         82        1           1743.49          0.39249  │
│ trial_fa758    TERMINATED           7   rmsprop         tanh                                   64                256                  5          0.00312316          70        1           1852.51          0.361462 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                   64                256                  5          0.00102495          49        1           1313.33          0.450198 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                  128                 64                  3          2.02194e-05         37        1            816.895         0.13834  │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                 32                  3          0.0073498           61        1            962.373         0.317984 │
│ trial_fa758    TERMINATED           5   adam            relu                                   64                 32                  3          0.000219563         45        1           1260.64          0.340514 │
│ trial_fa758    TERMINATED           6   rmsprop         tanh                                   32                128                  3          0.000110116         75        1            943.883         0.205534 │
│ trial_fa758    TERMINATED           6   adam            relu                                   64                128                  3          0.00500405          50        1            723.579         0.455731 │
│ trial_fa758    TERMINATED           6   rmsprop         relu                                   32                256                  5          7.32936e-05         76        1           2026.24          0.443676 │
│ trial_fa758    TERMINATED           5   rmsprop         tanh                                  128                128                  5          4.27402e-05         31        1           1220.97          0.228063 │
│ trial_fa758    TERMINATED           4   adam            tanh                                   64                256                  5          0.000389976         42        1           1369.69          0.457312 │
│ trial_fa758    TERMINATED           8   adam            relu                                   64                 32                  3          1.00577e-05         65        1            288.099         0.107115 │
│ trial_fa758    TERMINATED           4   adam            relu                                  128                256                  3          0.000235443         92        1            980.774         0.466403 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

[36m(train_cnn_ray_tune pid=571067)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Mejores hiperparámetros: {'N_capas': 4, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 128, 'numero_filtros': 256, 'tamanho_filtro': 3, 'tasa_aprendizaje': 0.00023544267794881635, 'epochs': 92}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564358.333279  660071 service.cc:152] XLA service 0x7d0a4c003300 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564358.333323  660071 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:25:58.388207: 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 00:00:1761564358.700718  660071 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564362.680466  660071 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:18[0m 6s/step - accuracy: 0.0547 - loss: 3.0571
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1105 - loss: 2.9272
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1151 - loss: 2.9040
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1186 - loss: 2.88372025-10-27 12:26:04.738921: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:26:07.404736: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads

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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 35ms/step - accuracy: 0.1219 - loss: 2.86432025-10-27 12:26:09.543134: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 54ms/step - accuracy: 0.1220 - loss: 2.8634 - val_accuracy: 0.2397 - val_loss: 2.0715
Epoch 2/92

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[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1542 - loss: 2.6136 
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1615 - loss: 2.5748
[1m 57/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1649 - loss: 2.5489
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1683 - loss: 2.5275
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1718 - loss: 2.5106
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1749 - loss: 2.4976
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.1778 - loss: 2.4849
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.1781 - loss: 2.4837 - val_accuracy: 0.2781 - val_loss: 1.8304
Epoch 3/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2269 - loss: 2.2664 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2231 - loss: 2.2725
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2220 - loss: 2.2729
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2225 - loss: 2.2669
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2236 - loss: 2.2598
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2253 - loss: 2.2522
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2266 - loss: 2.2464 - val_accuracy: 0.3245 - val_loss: 1.7081
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.0195
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2444 - loss: 2.1071 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2479 - loss: 2.1057
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2519 - loss: 2.0998
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2543 - loss: 2.0957
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2561 - loss: 2.0916
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2578 - loss: 2.0872
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2588 - loss: 2.0837 - val_accuracy: 0.3644 - val_loss: 1.5936
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 1.8629
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2858 - loss: 1.9568 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2881 - loss: 1.9583
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2886 - loss: 1.9640
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2892 - loss: 1.9665
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2898 - loss: 1.9661
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2907 - loss: 1.9646
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2914 - loss: 1.9628 - val_accuracy: 0.3700 - val_loss: 1.5315
Epoch 6/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3359 - loss: 1.8027
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3144 - loss: 1.8806 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3125 - loss: 1.8831
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3126 - loss: 1.8847
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3126 - loss: 1.8857
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3124 - loss: 1.8858
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3122 - loss: 1.8848
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3122 - loss: 1.8831 - val_accuracy: 0.3947 - val_loss: 1.4571
Epoch 7/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3123 - loss: 1.8320 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3170 - loss: 1.8213
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3200 - loss: 1.8190
[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3216 - loss: 1.8174
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Epoch 8/92

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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3338 - loss: 1.7794
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3358 - loss: 1.7754
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Epoch 9/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3443 - loss: 1.7233
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3453 - loss: 1.7197
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.7165
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3478 - loss: 1.7144 - val_accuracy: 0.4186 - val_loss: 1.3969
Epoch 10/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3838 - loss: 1.6185 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3839 - loss: 1.6276
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3839 - loss: 1.6340
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[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3836 - loss: 1.6367
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3838 - loss: 1.6370 - val_accuracy: 0.4342 - val_loss: 1.3916
Epoch 11/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 32ms/step - accuracy: 0.3750 - loss: 1.6751
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3776 - loss: 1.6322 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3804 - loss: 1.6314
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3841 - loss: 1.6277
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3855 - loss: 1.6251
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3865 - loss: 1.6220
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3869 - loss: 1.6205 - val_accuracy: 0.4393 - val_loss: 1.3780
Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4240 - loss: 1.5216 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4226 - loss: 1.5341
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4169 - loss: 1.5420
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4156 - loss: 1.5431
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4145 - loss: 1.5440
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4139 - loss: 1.5449 - val_accuracy: 0.4543 - val_loss: 1.3608
Epoch 13/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4200 - loss: 1.5204 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4200 - loss: 1.5155
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[1m 87/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4215 - loss: 1.5124
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[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4224 - loss: 1.5105
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Epoch 14/92

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Epoch 15/92

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Epoch 16/92

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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4496 - loss: 1.4376
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4480 - loss: 1.4367 - val_accuracy: 0.4575 - val_loss: 1.3588
Epoch 17/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4661 - loss: 1.4074 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4620 - loss: 1.4063
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4597 - loss: 1.4023
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4583 - loss: 1.4039
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4577 - loss: 1.4052
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4572 - loss: 1.4061 - val_accuracy: 0.4719 - val_loss: 1.3556
Epoch 18/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4713 - loss: 1.3651 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4669 - loss: 1.3668
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4615 - loss: 1.3728
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4601 - loss: 1.3743
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4598 - loss: 1.3748
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4597 - loss: 1.3750 - val_accuracy: 0.4621 - val_loss: 1.3533
Epoch 19/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4552 - loss: 1.3873 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4567 - loss: 1.3815
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4580 - loss: 1.3782
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Epoch 20/92

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Saved model to disk.
[36m(train_cnn_ray_tune pid=571067)[0m 
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[36m(train_cnn_ray_tune pid=571067)[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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m298/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 850us/step
[1m361/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 840us/step
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[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 818us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 59/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 866us/step
[1m124/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 820us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 46.58 [%]
Global F1 score (validation) = 45.49 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.3853030e-04 1.8164880e-03 7.3370006e-04 ... 1.8182142e-04
  5.1150814e-04 3.9567624e-04]
 [1.0943352e-03 2.1957653e-03 1.2888598e-03 ... 5.9859944e-04
  7.0245052e-04 3.3892257e-04]
 [9.9922100e-04 1.8543365e-03 1.1225836e-03 ... 6.6476263e-04
  5.5664266e-04 2.7837537e-04]
 ...
 [1.8619947e-05 8.8039415e-06 1.7782262e-05 ... 1.8316191e-03
  7.5443840e-04 1.0229243e-03]
 [2.3540224e-05 1.0659235e-05 2.5251626e-05 ... 1.8618734e-03
  5.5081927e-04 1.9865152e-03]
 [4.2824852e-04 3.3822318e-04 6.2319956e-04 ... 2.5053188e-01
  5.8212329e-04 2.9987625e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.88 [%]
Global accuracy score (test) = 46.91 [%]
Global F1 score (train) = 55.75 [%]
Global F1 score (test) = 44.76 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.39      0.27       161
 CAMINAR CON MÓVIL O LIBRO       0.19      0.14      0.16       161
       CAMINAR USUAL SPEED       0.23      0.19      0.21       161
            CAMINAR ZIGZAG       0.12      0.05      0.07       161
          DE PIE BARRIENDO       0.47      0.42      0.44       161
   DE PIE DOBLANDO TOALLAS       0.35      0.31      0.33       161
    DE PIE MOVIENDO LIBROS       0.38      0.48      0.42       161
          DE PIE USANDO PC       0.78      0.81      0.79       161
        FASE REPOSO CON K5       0.58      0.87      0.70       161
INCREMENTAL CICLOERGOMETRO       0.97      0.92      0.95       161
           SENTADO LEYENDO       0.39      0.74      0.51       161
         SENTADO USANDO PC       0.53      0.23      0.32       161
      SENTADO VIENDO LA TV       0.73      0.05      0.09       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.72      0.62       161
                    TROTAR       0.91      0.75      0.83       138

                  accuracy                           0.47      2392
                 macro avg       0.49      0.47      0.45      2392
              weighted avg       0.49      0.47      0.44      2392

2025-10-27 12:26:37.037733: 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`.
2025-10-27 12:26:37.050110: 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 00:00:1761564397.063667  663329 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564397.068022  663329 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564397.079204  663329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564397.079228  663329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564397.079232  663329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564397.079234  663329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:26:37.082452: 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 00:00:1761564399.604009  663329 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12390 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564402.207454  663467 service.cc:152] XLA service 0x7b554c002610 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564402.207493  663467 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:26:42.264820: 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 00:00:1761564402.560459  663467 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564406.540144  663467 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:57[0m 6s/step - accuracy: 0.0469 - loss: 3.2322
[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0742 - loss: 3.1194  
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[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1053 - loss: 2.9543
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1092 - loss: 2.93132025-10-27 12:26:48.439022: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:26:51.120497: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1121 - loss: 2.91332025-10-27 12:26:53.189166: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 53ms/step - accuracy: 0.1123 - loss: 2.9123 - val_accuracy: 0.2306 - val_loss: 2.1052
Epoch 2/92

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[1m109/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1852 - loss: 2.4982
[1m131/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1862 - loss: 2.4883
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1866 - loss: 2.4851 - val_accuracy: 0.2891 - val_loss: 1.8079
Epoch 3/92

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[1m 46/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2029 - loss: 2.3301
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[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2111 - loss: 2.3040
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2136 - loss: 2.2948
[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2157 - loss: 2.2860
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2168 - loss: 2.2813 - val_accuracy: 0.3354 - val_loss: 1.6846
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.2304
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2722 - loss: 2.1257 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2694 - loss: 2.1204
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2661 - loss: 2.1071
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2664 - loss: 2.1008
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2673 - loss: 2.0937
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2677 - loss: 2.0902 - val_accuracy: 0.3654 - val_loss: 1.5665
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.2578 - loss: 2.1473
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2696 - loss: 2.0481 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2759 - loss: 2.0180
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[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2830 - loss: 1.9926
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Epoch 6/92

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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3142 - loss: 1.8796
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3127 - loss: 1.8814
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Epoch 7/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3295 - loss: 1.7966
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3289 - loss: 1.7979
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3286 - loss: 1.7989
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3285 - loss: 1.7990
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.3285 - loss: 1.7989 - val_accuracy: 0.4202 - val_loss: 1.4429
Epoch 8/92

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[1m 18/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3451 - loss: 1.7394 
[1m 36/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3473 - loss: 1.7449
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[1m 76/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3511 - loss: 1.7415
[1m 96/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3516 - loss: 1.7418
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3518 - loss: 1.7416
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3521 - loss: 1.7407
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3521 - loss: 1.7405 - val_accuracy: 0.4253 - val_loss: 1.4262
Epoch 9/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3600 - loss: 1.6961 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3594 - loss: 1.6962
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3616 - loss: 1.6964
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3635 - loss: 1.6948
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3655 - loss: 1.6929
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3667 - loss: 1.6913
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3675 - loss: 1.6899 - val_accuracy: 0.4468 - val_loss: 1.3798
Epoch 10/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3744 - loss: 1.6496 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3747 - loss: 1.6529
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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3757 - loss: 1.6519
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Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4040 - loss: 1.5717 
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3995 - loss: 1.5844
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Epoch 12/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4057 - loss: 1.5667
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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4075 - loss: 1.5604
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Epoch 13/92

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[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4061 - loss: 1.5392
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[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4104 - loss: 1.5306
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4111 - loss: 1.5300
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Epoch 14/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4609 - loss: 1.3664
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4300 - loss: 1.4908 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4315 - loss: 1.4879
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4321 - loss: 1.4900
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4321 - loss: 1.4909
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4319 - loss: 1.4911
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4315 - loss: 1.4910 - val_accuracy: 0.4652 - val_loss: 1.3491
Epoch 15/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4310 - loss: 1.4874 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4343 - loss: 1.4734
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[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4353 - loss: 1.4664
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4361 - loss: 1.4643
[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4368 - loss: 1.4624
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4370 - loss: 1.4618 - val_accuracy: 0.4630 - val_loss: 1.3429
Epoch 16/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4375 - loss: 1.4937
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4246 - loss: 1.4823 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4275 - loss: 1.4725
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4329 - loss: 1.4578
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4339 - loss: 1.4552
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4347 - loss: 1.4534
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4357 - loss: 1.4518 - val_accuracy: 0.4480 - val_loss: 1.3645
Epoch 17/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4438 - loss: 1.4394 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4477 - loss: 1.4387
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[1m 89/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4492 - loss: 1.4333
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[1m129/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4501 - loss: 1.4292
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Epoch 18/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4569 - loss: 1.3949
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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4581 - loss: 1.3941
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Epoch 19/92

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[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4765 - loss: 1.3608
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Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4375 - loss: 1.3752
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4563 - loss: 1.3675 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4608 - loss: 1.3611
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4631 - loss: 1.3589
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4644 - loss: 1.3585
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4655 - loss: 1.3578
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4664 - loss: 1.3569
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4669 - loss: 1.3565 - val_accuracy: 0.4686 - val_loss: 1.3702

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 1s/step
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 857us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 1: 46.91 [%]
F1-score capturado en la ejecución 1: 44.76 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 57/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 899us/step
[1m121/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 838us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 46.86 [%]
Global F1 score (validation) = 45.37 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.5871938e-04 9.3877839e-04 3.9249987e-04 ... 3.6385047e-04
  6.4731331e-04 2.7248557e-04]
 [1.1354304e-03 2.4349436e-03 1.2562012e-03 ... 9.3379611e-04
  1.2675161e-03 3.5079740e-04]
 [5.6214945e-04 1.1915417e-03 5.9986743e-04 ... 8.1197056e-04
  9.2660420e-04 1.8655197e-04]
 ...
 [7.2882685e-06 6.3128264e-06 5.2601317e-06 ... 1.5090975e-03
  8.8228477e-04 5.2719138e-04]
 [9.6097165e-06 9.7448728e-06 8.5587635e-06 ... 3.0462767e-03
  8.5199636e-04 1.3681732e-03]
 [1.6704845e-04 1.5083615e-04 2.3273188e-04 ... 2.7895245e-01
  6.9976185e-04 1.8757306e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.24 [%]
Global accuracy score (test) = 46.2 [%]
Global F1 score (train) = 54.97 [%]
Global F1 score (test) = 43.92 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.25      0.25       161
 CAMINAR CON MÓVIL O LIBRO       0.23      0.26      0.24       161
       CAMINAR USUAL SPEED       0.29      0.27      0.28       161
            CAMINAR ZIGZAG       0.14      0.10      0.12       161
          DE PIE BARRIENDO       0.39      0.37      0.38       161
   DE PIE DOBLANDO TOALLAS       0.33      0.29      0.31       161
    DE PIE MOVIENDO LIBROS       0.36      0.35      0.36       161
          DE PIE USANDO PC       0.69      0.88      0.77       161
        FASE REPOSO CON K5       0.58      0.86      0.69       161
INCREMENTAL CICLOERGOMETRO       0.96      0.94      0.95       161
           SENTADO LEYENDO       0.39      0.76      0.52       161
         SENTADO USANDO PC       0.42      0.08      0.14       161
      SENTADO VIENDO LA TV       0.52      0.14      0.22       161
   SUBIR Y BAJAR ESCALERAS       0.49      0.66      0.57       161
                    TROTAR       0.87      0.75      0.80       138

                  accuracy                           0.46      2392
                 macro avg       0.46      0.46      0.44      2392
              weighted avg       0.46      0.46      0.44      2392

2025-10-27 12:27:17.732755: 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`.
2025-10-27 12:27:17.745634: 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 00:00:1761564437.759771  666670 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564437.764269  666670 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564437.775502  666670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564437.775528  666670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564437.775531  666670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564437.775533  666670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:27:17.779115: 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 00:00:1761564440.210800  666670 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564442.818522  666795 service.cc:152] XLA service 0x7da510002400 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564442.818564  666795 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:27:22.873684: 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 00:00:1761564443.183269  666795 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564447.079920  666795 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:49[0m 6s/step - accuracy: 0.0469 - loss: 3.3934
[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0682 - loss: 3.1868  
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0804 - loss: 3.1092
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0964 - loss: 3.0196
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1020 - loss: 2.9881
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1066 - loss: 2.96132025-10-27 12:27:29.082626: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:27:31.864738: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 35ms/step - accuracy: 0.1099 - loss: 2.94152025-10-27 12:27:34.044798: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 56ms/step - accuracy: 0.1101 - loss: 2.9403 - val_accuracy: 0.2006 - val_loss: 2.1637
Epoch 2/92

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[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1722 - loss: 2.5388
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Epoch 3/92

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Epoch 4/92

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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2464 - loss: 2.1340
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Epoch 5/92

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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2834 - loss: 1.9967
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Epoch 6/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2768 - loss: 1.9885 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2847 - loss: 1.9638
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[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2953 - loss: 1.9339
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2967 - loss: 1.9280
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2977 - loss: 1.9238 - val_accuracy: 0.3962 - val_loss: 1.5069
Epoch 7/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3349 - loss: 1.7935 
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3367 - loss: 1.7992
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3365 - loss: 1.8037
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3363 - loss: 1.8066
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3357 - loss: 1.8076
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3351 - loss: 1.8080
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3351 - loss: 1.8068 - val_accuracy: 0.4059 - val_loss: 1.4835
Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3335 - loss: 1.7655 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3372 - loss: 1.7592
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3406 - loss: 1.7591
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3412 - loss: 1.7584
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3418 - loss: 1.7574
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Epoch 9/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3748 - loss: 1.6794 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3686 - loss: 1.7009
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3634 - loss: 1.7123
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3607 - loss: 1.7135 - val_accuracy: 0.4194 - val_loss: 1.4370
Epoch 10/92

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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3709 - loss: 1.6710
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3717 - loss: 1.6688
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Epoch 11/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3909 - loss: 1.6261
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3912 - loss: 1.6184
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3914 - loss: 1.6160
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3914 - loss: 1.6143
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Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4143 - loss: 1.6042 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4102 - loss: 1.5827
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4098 - loss: 1.5798
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4093 - loss: 1.5779
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4089 - loss: 1.5765
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4089 - loss: 1.5765 - val_accuracy: 0.4409 - val_loss: 1.3846
Epoch 13/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3864 - loss: 1.5696 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3971 - loss: 1.5582
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4016 - loss: 1.5544
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4044 - loss: 1.5513
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4062 - loss: 1.5493
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4078 - loss: 1.5468
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4086 - loss: 1.5450 - val_accuracy: 0.4460 - val_loss: 1.3778
Epoch 14/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4166 - loss: 1.5623 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4163 - loss: 1.5439
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4198 - loss: 1.5264
[1m107/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4208 - loss: 1.5212
[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4212 - loss: 1.5176
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4213 - loss: 1.5158 - val_accuracy: 0.4480 - val_loss: 1.3806
Epoch 15/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4252 - loss: 1.5453 
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[1m 75/137[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4303 - loss: 1.5007
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Epoch 16/92

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Epoch 17/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4486 - loss: 1.4009 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4489 - loss: 1.4164
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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4502 - loss: 1.4178
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Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4422 - loss: 1.4046 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4490 - loss: 1.3994
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[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4541 - loss: 1.3913
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4548 - loss: 1.3903
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4551 - loss: 1.3902 - val_accuracy: 0.4632 - val_loss: 1.3822
Epoch 19/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4646 - loss: 1.3664 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4660 - loss: 1.3685
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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4670 - loss: 1.3665
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4672 - loss: 1.3670
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4675 - loss: 1.3675
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4676 - loss: 1.3675 - val_accuracy: 0.4644 - val_loss: 1.3821
Epoch 20/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4633 - loss: 1.3376 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4654 - loss: 1.3459
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4689 - loss: 1.3447
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4691 - loss: 1.3459
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4693 - loss: 1.3467
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4695 - loss: 1.3471 - val_accuracy: 0.4589 - val_loss: 1.3995
Epoch 21/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4825 - loss: 1.3458 
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4805 - loss: 1.3308
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Epoch 22/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5312 - loss: 1.2947
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:23[0m 1s/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 843us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 2: 46.2 [%]
F1-score capturado en la ejecución 2: 43.92 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 60/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 856us/step
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[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 794us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 807us/step
[1m126/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 805us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 47.11 [%]
Global F1 score (validation) = 46.27 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.8795082e-04 9.2897943e-04 5.1975378e-04 ... 2.0302950e-04
  3.0001783e-04 1.3181751e-04]
 [1.5603866e-03 2.6325099e-03 1.3811338e-03 ... 4.8083643e-04
  1.9419788e-03 2.0083154e-04]
 [1.0412369e-03 1.9583472e-03 1.0998241e-03 ... 4.7981864e-04
  1.6943412e-03 1.2918246e-04]
 ...
 [7.4823870e-06 6.3327957e-06 4.8334850e-06 ... 2.2242644e-03
  9.0361183e-04 6.5208139e-04]
 [1.0721430e-05 1.0322692e-05 7.9132560e-06 ... 3.7319935e-03
  8.9720311e-04 1.3736937e-03]
 [2.7013035e-04 2.4155041e-04 4.2965624e-04 ... 2.9833114e-01
  1.4307220e-03 4.1943598e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 58.76 [%]
Global accuracy score (test) = 44.73 [%]
Global F1 score (train) = 58.32 [%]
Global F1 score (test) = 43.86 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.37      0.30       161
 CAMINAR CON MÓVIL O LIBRO       0.24      0.20      0.22       161
       CAMINAR USUAL SPEED       0.32      0.34      0.33       161
            CAMINAR ZIGZAG       0.13      0.09      0.11       161
          DE PIE BARRIENDO       0.42      0.37      0.40       161
   DE PIE DOBLANDO TOALLAS       0.30      0.32      0.31       161
    DE PIE MOVIENDO LIBROS       0.41      0.43      0.42       161
          DE PIE USANDO PC       0.80      0.81      0.80       161
        FASE REPOSO CON K5       0.53      0.87      0.66       161
INCREMENTAL CICLOERGOMETRO       0.94      0.91      0.93       161
           SENTADO LEYENDO       0.30      0.32      0.31       161
         SENTADO USANDO PC       0.35      0.18      0.24       161
      SENTADO VIENDO LA TV       0.19      0.13      0.15       161
   SUBIR Y BAJAR ESCALERAS       0.49      0.68      0.57       161
                    TROTAR       0.95      0.72      0.82       138

                  accuracy                           0.45      2392
                 macro avg       0.44      0.45      0.44      2392
              weighted avg       0.44      0.45      0.43      2392

2025-10-27 12:27:59.635204: 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`.
2025-10-27 12:27:59.647627: 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 00:00:1761564479.661238  670180 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564479.665424  670180 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564479.676536  670180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564479.676558  670180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564479.676561  670180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564479.676570  670180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:27:59.679760: 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 00:00:1761564482.095532  670180 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564484.690058  670336 service.cc:152] XLA service 0x7d99b8024750 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564484.690129  670336 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:28:04.747516: 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 00:00:1761564485.047723  670336 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564489.050104  670336 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:01[0m 6s/step - accuracy: 0.1172 - loss: 3.0343
[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0857 - loss: 3.0668  
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0896 - loss: 3.0358
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0954 - loss: 3.0003
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1005 - loss: 2.9710
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1050 - loss: 2.9462
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1089 - loss: 2.92302025-10-27 12:28:10.954194: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:28:13.627249: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1117 - loss: 2.90722025-10-27 12:28:15.773771: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 54ms/step - accuracy: 0.1118 - loss: 2.9063 - val_accuracy: 0.2211 - val_loss: 2.1410
Epoch 2/92

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[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1776 - loss: 2.5109
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1789 - loss: 2.5031
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1802 - loss: 2.4958 - val_accuracy: 0.2874 - val_loss: 1.8196
Epoch 3/92

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[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2116 - loss: 2.3078
[1m 67/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2140 - loss: 2.3001
[1m 89/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2158 - loss: 2.2921
[1m109/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2176 - loss: 2.2830
[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2192 - loss: 2.2751
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2200 - loss: 2.2711 - val_accuracy: 0.3405 - val_loss: 1.7114
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.3203 - loss: 2.0608
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2722 - loss: 2.1287 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2679 - loss: 2.1208
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2626 - loss: 2.1225
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2601 - loss: 2.1212
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2590 - loss: 2.1187
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2587 - loss: 2.1153
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2586 - loss: 2.1125 - val_accuracy: 0.3595 - val_loss: 1.5781
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.0612
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2725 - loss: 2.0206 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2725 - loss: 2.0132
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[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2733 - loss: 2.0004
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Epoch 6/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2923 - loss: 1.8926
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Epoch 7/92

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[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 1.8028
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Epoch 8/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.8068 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.7931
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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3392 - loss: 1.7721
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3410 - loss: 1.7663
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3421 - loss: 1.7628
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3429 - loss: 1.7605 - val_accuracy: 0.4231 - val_loss: 1.4082
Epoch 9/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3775 - loss: 1.7100 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3686 - loss: 1.7144
[1m 65/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3669 - loss: 1.7104
[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3661 - loss: 1.7085
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3664 - loss: 1.7068
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3666 - loss: 1.7052
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3668 - loss: 1.7040 - val_accuracy: 0.4300 - val_loss: 1.4028
Epoch 10/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3766 - loss: 1.6612 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3815 - loss: 1.6475
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3826 - loss: 1.6397
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3828 - loss: 1.6386
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Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4019 - loss: 1.5892 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4031 - loss: 1.5828
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4027 - loss: 1.5798
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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4013 - loss: 1.5831
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4010 - loss: 1.5838 - val_accuracy: 0.4379 - val_loss: 1.3808
Epoch 12/92

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[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4046 - loss: 1.5640
[1m108/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4057 - loss: 1.5610
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4066 - loss: 1.5585
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Epoch 13/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4187 - loss: 1.5391
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4179 - loss: 1.5370
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4172 - loss: 1.5351
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4172 - loss: 1.5331 - val_accuracy: 0.4470 - val_loss: 1.3540
Epoch 14/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4348 - loss: 1.4906 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4328 - loss: 1.4841
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4289 - loss: 1.4799
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4287 - loss: 1.4786
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4289 - loss: 1.4772
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4290 - loss: 1.4764 - val_accuracy: 0.4593 - val_loss: 1.3615
Epoch 15/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4062 - loss: 1.6051
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4419 - loss: 1.4567 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4391 - loss: 1.4622
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4367 - loss: 1.4643
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4364 - loss: 1.4629
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4360 - loss: 1.4624
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4357 - loss: 1.4612
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4355 - loss: 1.4605 - val_accuracy: 0.4383 - val_loss: 1.3677
Epoch 16/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 1.5213
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4351 - loss: 1.4345 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4459 - loss: 1.4227
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4475 - loss: 1.4220
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4470 - loss: 1.4234
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4469 - loss: 1.4240
[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4469 - loss: 1.4243
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4468 - loss: 1.4244 - val_accuracy: 0.4502 - val_loss: 1.3708
Epoch 17/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4922 - loss: 1.5102
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4672 - loss: 1.4412 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4625 - loss: 1.4362
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4614 - loss: 1.4300
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4610 - loss: 1.4255
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4606 - loss: 1.4221
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4601 - loss: 1.4203
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Epoch 18/92

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[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 3: 44.73 [%]
F1-score capturado en la ejecución 3: 43.86 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 50/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step  
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[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 881us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 54/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 948us/step
[1m113/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 895us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 46.07 [%]
Global F1 score (validation) = 43.13 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.7473103e-04 7.4063282e-04 4.0810046e-04 ... 4.5506796e-04
  4.3349655e-04 2.3001352e-04]
 [5.1223225e-04 8.7052811e-04 5.5901299e-04 ... 7.1119721e-04
  6.3475245e-04 1.8539926e-04]
 [4.3128373e-04 6.7617674e-04 3.8096606e-04 ... 7.5828587e-04
  8.3848543e-04 1.4942639e-04]
 ...
 [1.3358999e-05 1.6725337e-05 7.4469490e-06 ... 1.1342568e-03
  5.1530538e-04 2.2786297e-03]
 [9.4936249e-06 1.1438879e-05 5.2818409e-06 ... 1.1043103e-03
  4.3697629e-04 1.6668785e-03]
 [5.7499274e-04 4.8939284e-04 6.8284350e-04 ... 3.0144724e-01
  9.3403092e-04 4.3594260e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 54.28 [%]
Global accuracy score (test) = 46.74 [%]
Global F1 score (train) = 51.89 [%]
Global F1 score (test) = 44.42 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.39      0.27       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.11      0.15       161
       CAMINAR USUAL SPEED       0.25      0.30      0.27       161
            CAMINAR ZIGZAG       0.19      0.05      0.08       161
          DE PIE BARRIENDO       0.48      0.43      0.45       161
   DE PIE DOBLANDO TOALLAS       0.37      0.39      0.38       161
    DE PIE MOVIENDO LIBROS       0.36      0.34      0.35       161
          DE PIE USANDO PC       0.71      0.83      0.76       161
        FASE REPOSO CON K5       0.62      0.86      0.72       161
INCREMENTAL CICLOERGOMETRO       0.95      0.93      0.94       161
           SENTADO LEYENDO       0.42      0.03      0.06       161
         SENTADO USANDO PC       0.37      0.66      0.48       161
      SENTADO VIENDO LA TV       0.41      0.28      0.33       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.75      0.61       161
                    TROTAR       0.96      0.70      0.81       138

                  accuracy                           0.47      2392
                 macro avg       0.47      0.47      0.44      2392
              weighted avg       0.47      0.47      0.44      2392

2025-10-27 12:28:39.190117: 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`.
2025-10-27 12:28:39.202573: 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 00:00:1761564519.216025  673306 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564519.220467  673306 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564519.231415  673306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564519.231439  673306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564519.231444  673306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564519.231447  673306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:28:39.234889: 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 00:00:1761564521.665310  673306 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12393 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564524.225994  673471 service.cc:152] XLA service 0x722dd80146d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564524.226062  673471 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:28:44.284704: 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 00:00:1761564524.582048  673471 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564528.594356  673471 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:56[0m 6s/step - accuracy: 0.1094 - loss: 3.0675
[1m 17/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0771 - loss: 3.1365  
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0893 - loss: 3.0617
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0978 - loss: 3.0161
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1050 - loss: 2.9799
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1104 - loss: 2.9487
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1141 - loss: 2.92552025-10-27 12:28:50.235506: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:28:53.259223: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 35ms/step - accuracy: 0.1171 - loss: 2.90732025-10-27 12:28:55.321426: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1173 - loss: 2.9062 - val_accuracy: 0.2399 - val_loss: 2.0781
Epoch 2/92

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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1874 - loss: 2.4854
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1876 - loss: 2.4818
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1879 - loss: 2.4772
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1884 - loss: 2.4720 - val_accuracy: 0.2779 - val_loss: 1.8795
Epoch 3/92

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[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2171 - loss: 2.2945
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2188 - loss: 2.2870
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2201 - loss: 2.2802
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2210 - loss: 2.2754
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2220 - loss: 2.2709 - val_accuracy: 0.3142 - val_loss: 1.7212
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0569
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2479 - loss: 2.1344 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2497 - loss: 2.1300
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2502 - loss: 2.1276
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2509 - loss: 2.1236
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2515 - loss: 2.1197
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.2526 - loss: 2.1147
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2536 - loss: 2.1105 - val_accuracy: 0.3540 - val_loss: 1.6513
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.2891 - loss: 2.0368
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2714 - loss: 2.0326 
[1m 46/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2752 - loss: 2.0228
[1m 68/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2778 - loss: 2.0151
[1m 91/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2800 - loss: 2.0074
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2816 - loss: 2.0014
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2824 - loss: 1.9970
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2824 - loss: 1.9968 - val_accuracy: 0.3706 - val_loss: 1.5551
Epoch 6/92

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Epoch 7/92

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Epoch 8/92

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Epoch 9/92

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[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3639 - loss: 1.7088
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Epoch 10/92

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[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3826 - loss: 1.6299
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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3817 - loss: 1.6340
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3815 - loss: 1.6358 - val_accuracy: 0.4336 - val_loss: 1.3978
Epoch 11/92

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Epoch 12/92

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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4062 - loss: 1.5897
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Epoch 13/92

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Epoch 14/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4080 - loss: 1.4865 
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4200 - loss: 1.4792
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[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4222 - loss: 1.4794
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Epoch 15/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4512 - loss: 1.4557 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4454 - loss: 1.4536
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4403 - loss: 1.4547
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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4378 - loss: 1.4571
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4377 - loss: 1.4568 - val_accuracy: 0.4615 - val_loss: 1.3544
Epoch 16/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4357 - loss: 1.4359 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4406 - loss: 1.4360
[1m 67/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4428 - loss: 1.4332
[1m 88/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4437 - loss: 1.4311
[1m109/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4440 - loss: 1.4291
[1m129/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.4282
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4445 - loss: 1.4280 - val_accuracy: 0.4739 - val_loss: 1.3451
Epoch 17/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4209 - loss: 1.4822 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4314 - loss: 1.4569
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4395 - loss: 1.4386
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[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4424 - loss: 1.4322
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Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4479 - loss: 1.4092 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4531 - loss: 1.4024
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4550 - loss: 1.4015
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4568 - loss: 1.3991
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4580 - loss: 1.3967
[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4583 - loss: 1.3956
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4583 - loss: 1.3954 - val_accuracy: 0.4451 - val_loss: 1.3976
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5234 - loss: 1.3180
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[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4660 - loss: 1.3605
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4660 - loss: 1.3591
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4654 - loss: 1.3599
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4649 - loss: 1.3603
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4648 - loss: 1.3608
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4648 - loss: 1.3608 - val_accuracy: 0.4676 - val_loss: 1.3746
Epoch 20/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4779 - loss: 1.3192
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4757 - loss: 1.3267
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4743 - loss: 1.3311
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4737 - loss: 1.3342
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4737 - loss: 1.3353 - val_accuracy: 0.4704 - val_loss: 1.3838
Epoch 21/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4766 - loss: 1.2817
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4849 - loss: 1.3052 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4857 - loss: 1.3072
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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4823 - loss: 1.3172
[1m107/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4818 - loss: 1.3199
[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4813 - loss: 1.3216
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4811 - loss: 1.3220 - val_accuracy: 0.4664 - val_loss: 1.3983

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:17[0m 1s/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 843us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 4: 46.74 [%]
F1-score capturado en la ejecución 4: 44.42 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m186/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 816us/step
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[1m375/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 808us/step
[1m441/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 801us/step
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 912us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 65/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 786us/step
[1m133/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 760us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 46.64 [%]
Global F1 score (validation) = 45.34 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.1856696e-03 1.3873128e-03 6.0168916e-04 ... 4.9086410e-04
  1.4386484e-03 3.5261884e-04]
 [2.4989552e-03 3.2796292e-03 1.7998852e-03 ... 1.2780697e-03
  2.9373143e-03 9.4977807e-04]
 [6.1970280e-04 1.0092215e-03 4.1272130e-04 ... 7.1005448e-04
  1.1790879e-03 2.2304915e-04]
 ...
 [1.6744300e-05 4.9048299e-06 6.0743100e-06 ... 1.1791524e-03
  6.7669858e-04 7.5044931e-04]
 [2.1963460e-05 6.2175163e-06 8.8347988e-06 ... 2.0894199e-03
  7.5762416e-04 2.1085362e-03]
 [6.0720008e-04 2.3603652e-04 4.7913610e-04 ... 2.9708198e-01
  8.8188751e-04 3.0180535e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 57.33 [%]
Global accuracy score (test) = 45.36 [%]
Global F1 score (train) = 56.09 [%]
Global F1 score (test) = 44.06 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.40      0.30       161
 CAMINAR CON MÓVIL O LIBRO       0.21      0.25      0.23       161
       CAMINAR USUAL SPEED       0.36      0.25      0.30       161
            CAMINAR ZIGZAG       0.17      0.06      0.08       161
          DE PIE BARRIENDO       0.39      0.35      0.37       161
   DE PIE DOBLANDO TOALLAS       0.31      0.24      0.27       161
    DE PIE MOVIENDO LIBROS       0.32      0.41      0.36       161
          DE PIE USANDO PC       0.70      0.84      0.76       161
        FASE REPOSO CON K5       0.54      0.88      0.67       161
INCREMENTAL CICLOERGOMETRO       0.96      0.92      0.94       161
           SENTADO LEYENDO       0.36      0.43      0.40       161
         SENTADO USANDO PC       0.49      0.26      0.34       161
      SENTADO VIENDO LA TV       0.27      0.14      0.19       161
   SUBIR Y BAJAR ESCALERAS       0.51      0.66      0.58       161
                    TROTAR       0.94      0.76      0.84       138

                  accuracy                           0.45      2392
                 macro avg       0.45      0.46      0.44      2392
              weighted avg       0.45      0.45      0.44      2392

2025-10-27 12:29:20.065907: 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`.
2025-10-27 12:29:20.084123: 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 00:00:1761564560.104601  676712 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564560.108754  676712 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564560.119728  676712 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564560.119761  676712 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564560.119764  676712 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564560.119766  676712 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:29:20.122966: 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 00:00:1761564562.532183  676712 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564565.109874  676872 service.cc:152] XLA service 0x76fd8c003020 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564565.109969  676872 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:29:25.173656: 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 00:00:1761564565.473823  676872 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564569.528521  676872 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:05[0m 6s/step - accuracy: 0.1016 - loss: 3.0236
[1m 17/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0749 - loss: 3.0930  
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0841 - loss: 3.0446
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0982 - loss: 2.9749
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1040 - loss: 2.9449
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1078 - loss: 2.92302025-10-27 12:29:31.321649: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:29:34.165514: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1109 - loss: 2.90552025-10-27 12:29:36.305744: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 54ms/step - accuracy: 0.1110 - loss: 2.9045 - val_accuracy: 0.2204 - val_loss: 2.1325
Epoch 2/92

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[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1790 - loss: 2.4939
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1813 - loss: 2.4775 - val_accuracy: 0.2690 - val_loss: 1.8644
Epoch 3/92

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Epoch 4/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2481 - loss: 2.1204
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Epoch 5/92

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[1m 89/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2783 - loss: 1.9891
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[1m132/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2794 - loss: 1.9819
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Epoch 6/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3159 - loss: 1.8760 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3129 - loss: 1.8744
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3119 - loss: 1.8727
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3115 - loss: 1.8722
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3115 - loss: 1.8718
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3117 - loss: 1.8715 - val_accuracy: 0.3941 - val_loss: 1.4816
Epoch 7/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3322 - loss: 1.8325 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3308 - loss: 1.8261
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3297 - loss: 1.8228
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3288 - loss: 1.8202
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3280 - loss: 1.8190
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3273 - loss: 1.8183
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3273 - loss: 1.8168 - val_accuracy: 0.4038 - val_loss: 1.4459
Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3395 - loss: 1.7810 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3367 - loss: 1.7858
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3398 - loss: 1.7829
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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3427 - loss: 1.7747
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Epoch 9/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3563 - loss: 1.7051
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3572 - loss: 1.7042
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Epoch 10/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3779 - loss: 1.6567
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Epoch 11/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4109 - loss: 1.6022 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3981 - loss: 1.6094
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3909 - loss: 1.6162
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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3899 - loss: 1.6150
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3900 - loss: 1.6139 - val_accuracy: 0.4589 - val_loss: 1.3582
Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4089 - loss: 1.5633 
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4111 - loss: 1.5596
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4112 - loss: 1.5608
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4117 - loss: 1.5595
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4120 - loss: 1.5580
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4119 - loss: 1.5576 - val_accuracy: 0.4470 - val_loss: 1.3830
Epoch 13/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4319 - loss: 1.5027 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4296 - loss: 1.5051
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4248 - loss: 1.5155
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4235 - loss: 1.5179
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4228 - loss: 1.5188
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4221 - loss: 1.5194 - val_accuracy: 0.4549 - val_loss: 1.3573
Epoch 14/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.4835 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4376 - loss: 1.4899
[1m 64/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4349 - loss: 1.4888
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4332 - loss: 1.4884
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4319 - loss: 1.4883
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4308 - loss: 1.4883
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4302 - loss: 1.4883 - val_accuracy: 0.4589 - val_loss: 1.3583
Epoch 15/92

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[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4340 - loss: 1.4588 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4311 - loss: 1.4626
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4321 - loss: 1.4627
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4322 - loss: 1.4639
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Epoch 16/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4327 - loss: 1.4470
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Epoch 17/92

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[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4573 - loss: 1.4107
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Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4596 - loss: 1.4263 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4570 - loss: 1.4145
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4593 - loss: 1.4042
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4597 - loss: 1.4021
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4603 - loss: 1.3998
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4609 - loss: 1.3975 - val_accuracy: 0.4628 - val_loss: 1.3714

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 1s/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 899us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/step
Saved model to disk.

Accuracy capturado en la ejecución 5: 45.36 [%]
F1-score capturado en la ejecución 5: 44.06 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 902us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 815us/step
[1m126/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 805us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 46.28 [%]
Global F1 score (validation) = 44.53 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.4776041e-04 8.9842087e-04 6.3990115e-04 ... 7.3968724e-04
  7.2247692e-04 2.4139415e-04]
 [1.1899592e-03 2.4986365e-03 1.4110338e-03 ... 1.0795196e-03
  2.0113292e-03 3.2274114e-04]
 [3.3804367e-04 7.0320029e-04 4.2886537e-04 ... 6.6229620e-04
  6.8118400e-04 1.2057169e-04]
 ...
 [6.7619771e-06 4.3342739e-06 6.4013911e-06 ... 1.0311484e-03
  8.4766862e-04 7.5829786e-04]
 [7.7630993e-06 4.8594875e-06 7.2082407e-06 ... 1.4964509e-03
  6.3448434e-04 1.2317689e-03]
 [5.9879059e-04 2.4517981e-04 7.1815308e-04 ... 3.0546665e-01
  5.2764348e-04 3.6764492e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.36 [%]
Global accuracy score (test) = 45.99 [%]
Global F1 score (train) = 54.36 [%]
Global F1 score (test) = 44.38 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.17      0.19       161
 CAMINAR CON MÓVIL O LIBRO       0.26      0.32      0.28       161
       CAMINAR USUAL SPEED       0.29      0.25      0.27       161
            CAMINAR ZIGZAG       0.25      0.24      0.25       161
          DE PIE BARRIENDO       0.43      0.45      0.44       161
   DE PIE DOBLANDO TOALLAS       0.33      0.20      0.25       161
    DE PIE MOVIENDO LIBROS       0.35      0.48      0.41       161
          DE PIE USANDO PC       0.75      0.83      0.79       161
        FASE REPOSO CON K5       0.52      0.87      0.65       161
INCREMENTAL CICLOERGOMETRO       0.97      0.93      0.95       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.60      0.42      0.49       161
      SENTADO VIENDO LA TV       0.25      0.36      0.29       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.68      0.59       161
                    TROTAR       0.89      0.74      0.81       138

                  accuracy                           0.46      2392
                 macro avg       0.44      0.46      0.44      2392
              weighted avg       0.44      0.46      0.44      2392

2025-10-27 12:29:59.962726: 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`.
2025-10-27 12:29:59.975213: 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 00:00:1761564599.988943  679858 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564599.993277  679858 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564600.004588  679858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564600.004618  679858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564600.004621  679858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564600.004624  679858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:30:00.008047: 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 00:00:1761564602.410496  679858 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564605.022741  680010 service.cc:152] XLA service 0x785ee0011dd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564605.022811  680010 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:30:05.075791: 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 00:00:1761564605.371913  680010 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564609.336930  680010 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:54[0m 6s/step - accuracy: 0.0703 - loss: 3.3229
[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0829 - loss: 3.0966  
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0930 - loss: 3.0274
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[1m 77/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1035 - loss: 2.9638
[1m 96/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1071 - loss: 2.9411
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1105 - loss: 2.9194
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.1136 - loss: 2.89902025-10-27 12:30:11.253778: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:30:13.933176: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1138 - loss: 2.89802025-10-27 12:30:16.076648: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 53ms/step - accuracy: 0.1139 - loss: 2.8970 - val_accuracy: 0.2231 - val_loss: 2.0813
Epoch 2/92

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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1741 - loss: 2.5308
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1757 - loss: 2.5190
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Epoch 3/92

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Epoch 4/92

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Epoch 5/92

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[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.2858 - loss: 1.9703
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Epoch 6/92

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Epoch 7/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3250 - loss: 1.8256
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3255 - loss: 1.8192
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3266 - loss: 1.8119 - val_accuracy: 0.4134 - val_loss: 1.4555
Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3526 - loss: 1.7306 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3490 - loss: 1.7452
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3463 - loss: 1.7501
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Epoch 9/92

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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3587 - loss: 1.7042
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3595 - loss: 1.7032
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Epoch 10/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3686 - loss: 1.6767
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3701 - loss: 1.6727
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Epoch 11/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3898 - loss: 1.6037 
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[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3915 - loss: 1.6005
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3917 - loss: 1.5980 - val_accuracy: 0.4300 - val_loss: 1.3919
Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4090 - loss: 1.5334 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4148 - loss: 1.5344
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4130 - loss: 1.5411
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4123 - loss: 1.5437
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4117 - loss: 1.5456 - val_accuracy: 0.4441 - val_loss: 1.3675
Epoch 13/92

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[1m 24/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4034 - loss: 1.5525 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4065 - loss: 1.5470
[1m 67/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4089 - loss: 1.5417
[1m 87/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4102 - loss: 1.5379
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4117 - loss: 1.5346
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4130 - loss: 1.5324
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4135 - loss: 1.5314 - val_accuracy: 0.4490 - val_loss: 1.3703
Epoch 14/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4210 - loss: 1.4939 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4220 - loss: 1.4960
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4268 - loss: 1.4929
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4286 - loss: 1.4914
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4295 - loss: 1.4904
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Epoch 15/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4458 - loss: 1.4371 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4381 - loss: 1.4540
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4378 - loss: 1.4549
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4380 - loss: 1.4548
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4382 - loss: 1.4547 - val_accuracy: 0.4644 - val_loss: 1.3609
Epoch 16/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.4297 - loss: 1.4408
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4388 - loss: 1.4467 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4381 - loss: 1.4479
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4389 - loss: 1.4464
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4392 - loss: 1.4453
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4396 - loss: 1.4444
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4404 - loss: 1.4425
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4411 - loss: 1.4412 - val_accuracy: 0.4688 - val_loss: 1.3665
Epoch 17/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4844 - loss: 1.2944
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4468 - loss: 1.4011 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4489 - loss: 1.4125
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4507 - loss: 1.4184
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4513 - loss: 1.4180
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4515 - loss: 1.4178
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4516 - loss: 1.4179 - val_accuracy: 0.4542 - val_loss: 1.3672
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.4531 - loss: 1.5458
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4662 - loss: 1.4156 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.4126
[1m 64/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4598 - loss: 1.4107
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4591 - loss: 1.4075
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4591 - loss: 1.4048
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4593 - loss: 1.4024
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4593 - loss: 1.4008 - val_accuracy: 0.4575 - val_loss: 1.3816
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4453 - loss: 1.4128
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4493 - loss: 1.4124 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4563 - loss: 1.3988
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4602 - loss: 1.3931
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4616 - loss: 1.3890
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4626 - loss: 1.3846
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4635 - loss: 1.3813
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4640 - loss: 1.3795 - val_accuracy: 0.4767 - val_loss: 1.3693
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.4922 - loss: 1.3742
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4684 - loss: 1.3715 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4713 - loss: 1.3588
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4734 - loss: 1.3549
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4752 - loss: 1.3536
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4761 - loss: 1.3533
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4768 - loss: 1.3528
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4770 - loss: 1.3524 - val_accuracy: 0.4796 - val_loss: 1.3731

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 1s/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 808us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/step
Saved model to disk.

Accuracy capturado en la ejecución 6: 45.99 [%]
F1-score capturado en la ejecución 6: 44.38 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:40[0m 1s/step
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[1m242/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 839us/step
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[1m352/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 863us/step
[1m408/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 868us/step
[1m467/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 866us/step
[1m527/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 863us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 776us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 62/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 825us/step
[1m124/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 820us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 47.96 [%]
Global F1 score (validation) = 46.33 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.2967933e-03 8.8707503e-04 6.4046780e-04 ... 2.5632701e-04
  9.3490566e-04 6.2731799e-04]
 [2.5819605e-03 4.2720689e-03 3.0172018e-03 ... 1.4306928e-03
  1.6265272e-03 7.4474263e-04]
 [6.3472212e-04 9.2186010e-04 6.5497245e-04 ... 4.5724076e-04
  5.2425312e-04 2.2180349e-04]
 ...
 [1.9622623e-05 1.2476633e-05 1.0528685e-05 ... 4.0587429e-03
  9.9831820e-04 1.1057659e-03]
 [2.2194143e-05 1.2490720e-05 1.2700979e-05 ... 2.9702249e-03
  1.1756282e-03 1.9055004e-03]
 [2.9828792e-04 2.6901349e-04 2.7970775e-04 ... 2.5506026e-01
  1.1904500e-03 2.5537324e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.84 [%]
Global accuracy score (test) = 44.98 [%]
Global F1 score (train) = 55.32 [%]
Global F1 score (test) = 43.13 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.10      0.13       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.27      0.26       161
       CAMINAR USUAL SPEED       0.25      0.33      0.28       161
            CAMINAR ZIGZAG       0.16      0.13      0.14       161
          DE PIE BARRIENDO       0.40      0.41      0.41       161
   DE PIE DOBLANDO TOALLAS       0.33      0.28      0.30       161
    DE PIE MOVIENDO LIBROS       0.35      0.43      0.39       161
          DE PIE USANDO PC       0.77      0.81      0.79       161
        FASE REPOSO CON K5       0.51      0.87      0.64       161
INCREMENTAL CICLOERGOMETRO       0.99      0.94      0.97       161
           SENTADO LEYENDO       0.37      0.55      0.44       161
         SENTADO USANDO PC       0.61      0.07      0.12       161
      SENTADO VIENDO LA TV       0.24      0.14      0.18       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.74      0.61       161
                    TROTAR       0.93      0.72      0.81       138

                  accuracy                           0.45      2392
                 macro avg       0.46      0.45      0.43      2392
              weighted avg       0.45      0.45      0.43      2392

2025-10-27 12:30:40.388051: 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`.
2025-10-27 12:30:40.400697: 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 00:00:1761564640.414510  683180 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564640.418955  683180 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564640.430071  683180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564640.430095  683180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564640.430098  683180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564640.430100  683180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:30:40.433526: 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 00:00:1761564642.840089  683180 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564645.428426  683326 service.cc:152] XLA service 0x78d880013520 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564645.428474  683326 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:30:45.485747: 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 00:00:1761564645.798151  683326 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564649.701618  683326 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:46[0m 6s/step - accuracy: 0.0391 - loss: 3.2648
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0702 - loss: 3.1291  
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0816 - loss: 3.0609
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0892 - loss: 3.0144
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0949 - loss: 2.9821
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0999 - loss: 2.9542
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.1042 - loss: 2.93032025-10-27 12:30:51.413603: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:30:54.320421: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads

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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1064 - loss: 2.91792025-10-27 12:30:56.508902: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads

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Epoch 2/92

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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1715 - loss: 2.5419
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1729 - loss: 2.5318
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Epoch 3/92

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[1m 92/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2085 - loss: 2.2865
[1m113/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2113 - loss: 2.2807
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Epoch 4/92

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[1m 88/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2503 - loss: 2.1279
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2532 - loss: 2.1153 - val_accuracy: 0.3472 - val_loss: 1.6241
Epoch 5/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2762 - loss: 2.0324
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2757 - loss: 2.0290
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2760 - loss: 2.0219
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2758 - loss: 2.0167
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.2754 - loss: 2.0126
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2753 - loss: 2.0101 - val_accuracy: 0.3587 - val_loss: 1.5530
Epoch 6/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3144 - loss: 1.9308 
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[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3115 - loss: 1.9030
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3104 - loss: 1.9005
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3097 - loss: 1.8986
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3096 - loss: 1.8953 - val_accuracy: 0.3982 - val_loss: 1.4935
Epoch 7/92

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[1m 47/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3191 - loss: 1.8250
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[1m 90/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3220 - loss: 1.8210
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[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 1.8167
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Epoch 8/92

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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3410 - loss: 1.7580
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Epoch 9/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3670 - loss: 1.7013
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3674 - loss: 1.6994
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3681 - loss: 1.6956 - val_accuracy: 0.4215 - val_loss: 1.4091
Epoch 10/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3977 - loss: 1.5982 
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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3849 - loss: 1.6393
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3847 - loss: 1.6391 - val_accuracy: 0.4304 - val_loss: 1.3837
Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3820 - loss: 1.6509 
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[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3948 - loss: 1.6084
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3944 - loss: 1.6069
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.3944 - loss: 1.6067 - val_accuracy: 0.4415 - val_loss: 1.3712
Epoch 12/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4216 - loss: 1.5317 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4204 - loss: 1.5344
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4175 - loss: 1.5397
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4161 - loss: 1.5419
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4152 - loss: 1.5434
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4147 - loss: 1.5444 - val_accuracy: 0.4304 - val_loss: 1.4247
Epoch 13/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3934 - loss: 1.5901 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3986 - loss: 1.5739
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4026 - loss: 1.5546
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[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4057 - loss: 1.5457
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4064 - loss: 1.5438 - val_accuracy: 0.4611 - val_loss: 1.3599
Epoch 14/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.5078 - loss: 1.4171
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4478 - loss: 1.4641 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4439 - loss: 1.4702
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4414 - loss: 1.4743
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4400 - loss: 1.4762
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4382 - loss: 1.4781
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4369 - loss: 1.4790
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4363 - loss: 1.4793 - val_accuracy: 0.4543 - val_loss: 1.3639
Epoch 15/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.5000 - loss: 1.3660
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4375 - loss: 1.4423 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4375 - loss: 1.4444
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4376 - loss: 1.4473
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4380 - loss: 1.4472
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4382 - loss: 1.4477
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4383 - loss: 1.4480
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4385 - loss: 1.4480 - val_accuracy: 0.4581 - val_loss: 1.3673
Epoch 16/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3828 - loss: 1.4367
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4406 - loss: 1.4062 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4443 - loss: 1.4117
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4455 - loss: 1.4129
[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4464 - loss: 1.4151
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4465 - loss: 1.4170
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4463 - loss: 1.4194
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4463 - loss: 1.4206 - val_accuracy: 0.4425 - val_loss: 1.3933
Epoch 17/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4375 - loss: 1.4936
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4351 - loss: 1.4433 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4445 - loss: 1.4252
[1m 64/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4490 - loss: 1.4158
[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4505 - loss: 1.4121
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4510 - loss: 1.4112
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4511 - loss: 1.4115
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4514 - loss: 1.4113 - val_accuracy: 0.4646 - val_loss: 1.3714
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4922 - loss: 1.2429
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4498 - loss: 1.3963 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4478 - loss: 1.4035
[1m 65/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4498 - loss: 1.4035
[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4516 - loss: 1.4008
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4532 - loss: 1.3979
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4540 - loss: 1.3958
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4545 - loss: 1.3950 - val_accuracy: 0.4571 - val_loss: 1.3767

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 1s/step
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 926us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 7: 44.98 [%]
F1-score capturado en la ejecución 7: 43.13 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:39[0m 1s/step
[1m 54/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 960us/step
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[1m179/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 855us/step
[1m244/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 834us/step
[1m304/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 836us/step
[1m363/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 838us/step
[1m421/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 843us/step
[1m484/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 838us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 839us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 53/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 972us/step
[1m112/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 910us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 45.71 [%]
Global F1 score (validation) = 42.84 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[9.3366194e-04 1.5572999e-03 7.3946308e-04 ... 2.8611455e-04
  7.5347011e-04 1.7288848e-04]
 [1.3609307e-03 2.2172623e-03 1.3386590e-03 ... 9.1816060e-04
  1.4379922e-03 2.8069344e-04]
 [1.1711702e-03 1.7841376e-03 1.3288624e-03 ... 6.5898965e-04
  1.0758224e-03 1.5768573e-04]
 ...
 [4.2611915e-05 2.9347020e-05 2.7372434e-05 ... 3.2864190e-03
  7.7499449e-04 3.3627728e-03]
 [2.9868743e-05 2.1655705e-05 1.9773179e-05 ... 3.0188938e-03
  6.1110337e-04 2.3369465e-03]
 [7.0996518e-04 4.4921425e-04 8.0327544e-04 ... 3.0157325e-01
  9.2023908e-04 3.9497940e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 54.83 [%]
Global accuracy score (test) = 47.83 [%]
Global F1 score (train) = 52.81 [%]
Global F1 score (test) = 46.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.06      0.10       161
 CAMINAR CON MÓVIL O LIBRO       0.23      0.32      0.27       161
       CAMINAR USUAL SPEED       0.28      0.55      0.37       161
            CAMINAR ZIGZAG       0.36      0.13      0.19       161
          DE PIE BARRIENDO       0.46      0.49      0.47       161
   DE PIE DOBLANDO TOALLAS       0.33      0.33      0.33       161
    DE PIE MOVIENDO LIBROS       0.38      0.40      0.39       161
          DE PIE USANDO PC       0.76      0.83      0.79       161
        FASE REPOSO CON K5       0.52      0.86      0.65       161
INCREMENTAL CICLOERGOMETRO       0.97      0.95      0.96       161
           SENTADO LEYENDO       0.41      0.27      0.32       161
         SENTADO USANDO PC       0.54      0.49      0.52       161
      SENTADO VIENDO LA TV       0.27      0.18      0.22       161
   SUBIR Y BAJAR ESCALERAS       0.55      0.59      0.57       161
                    TROTAR       0.93      0.77      0.84       138

                  accuracy                           0.48      2392
                 macro avg       0.48      0.48      0.47      2392
              weighted avg       0.48      0.48      0.46      2392

2025-10-27 12:31:19.994809: 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`.
2025-10-27 12:31:20.007603: 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 00:00:1761564680.021575  686314 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564680.025796  686314 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564680.037007  686314 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564680.037030  686314 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564680.037033  686314 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564680.037036  686314 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:31:20.040422: 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 00:00:1761564682.449420  686314 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564685.052909  686451 service.cc:152] XLA service 0x743ea4015600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564685.052987  686451 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:31:25.111869: 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 00:00:1761564685.426342  686451 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564689.429068  686451 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:04[0m 6s/step - accuracy: 0.0938 - loss: 3.0991
[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0823 - loss: 3.0830  
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0908 - loss: 3.0380
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0988 - loss: 3.0008
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1041 - loss: 2.9725
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1084 - loss: 2.9476
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1125 - loss: 2.92412025-10-27 12:31:31.110855: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:31:34.048913: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1157 - loss: 2.90542025-10-27 12:31:36.228586: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 54ms/step - accuracy: 0.1158 - loss: 2.9044 - val_accuracy: 0.2126 - val_loss: 2.1218
Epoch 2/92

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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1708 - loss: 2.5238
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1734 - loss: 2.5137
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Epoch 3/92

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Epoch 4/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.1162 
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[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2590 - loss: 2.0980
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Epoch 5/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2755 - loss: 2.0102 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2744 - loss: 2.0031
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2758 - loss: 1.9975
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2780 - loss: 1.9908
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2789 - loss: 1.9875
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2796 - loss: 1.9854
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2805 - loss: 1.9829 - val_accuracy: 0.3569 - val_loss: 1.5649
Epoch 6/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 1.8523 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3029 - loss: 1.8651
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3043 - loss: 1.8695
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3050 - loss: 1.8727
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3055 - loss: 1.8733
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3059 - loss: 1.8736
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3061 - loss: 1.8736 - val_accuracy: 0.3854 - val_loss: 1.5036
Epoch 7/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3467 - loss: 1.7705 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3375 - loss: 1.7932
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3327 - loss: 1.8016
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Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3613 - loss: 1.7378 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3498 - loss: 1.7625
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3482 - loss: 1.7621
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3476 - loss: 1.7613
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3472 - loss: 1.7603 - val_accuracy: 0.4281 - val_loss: 1.4031
Epoch 9/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3767 - loss: 1.6719
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3746 - loss: 1.6770
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3733 - loss: 1.6801
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Epoch 10/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3818 - loss: 1.6528 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3796 - loss: 1.6540
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3789 - loss: 1.6532
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3786 - loss: 1.6536
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.3785 - loss: 1.6529
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3786 - loss: 1.6524 - val_accuracy: 0.4387 - val_loss: 1.3911
Epoch 11/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3841 - loss: 1.6144 
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3855 - loss: 1.6136
[1m 57/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3838 - loss: 1.6169
[1m 76/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3835 - loss: 1.6187
[1m 94/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3835 - loss: 1.6200
[1m114/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3842 - loss: 1.6191
[1m134/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3851 - loss: 1.6173
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.3852 - loss: 1.6170 - val_accuracy: 0.4413 - val_loss: 1.3845
Epoch 12/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 1.5907
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3875 - loss: 1.6057 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3953 - loss: 1.5941
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3997 - loss: 1.5870
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4018 - loss: 1.5822
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4033 - loss: 1.5790
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4046 - loss: 1.5756
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4051 - loss: 1.5737 - val_accuracy: 0.4476 - val_loss: 1.3671
Epoch 13/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4531 - loss: 1.3343
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4337 - loss: 1.4933 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4313 - loss: 1.5044
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4281 - loss: 1.5101
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4265 - loss: 1.5123
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4259 - loss: 1.5132
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4256 - loss: 1.5132
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4250 - loss: 1.5134 - val_accuracy: 0.4567 - val_loss: 1.3515
Epoch 14/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4531 - loss: 1.4664
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4375 - loss: 1.4689 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4323 - loss: 1.4829
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4312 - loss: 1.4862
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4294 - loss: 1.4889
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4279 - loss: 1.4915
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4278 - loss: 1.4916
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4279 - loss: 1.4912 - val_accuracy: 0.4581 - val_loss: 1.3525
Epoch 15/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4766 - loss: 1.4080
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4442 - loss: 1.4641 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4409 - loss: 1.4575
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4394 - loss: 1.4554
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4383 - loss: 1.4579
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4375 - loss: 1.4583
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4367 - loss: 1.4589
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4359 - loss: 1.4594
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4358 - loss: 1.4594 - val_accuracy: 0.4587 - val_loss: 1.3599
Epoch 16/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4609 - loss: 1.4241
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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4408 - loss: 1.4550
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4418 - loss: 1.4480
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4418 - loss: 1.4446
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4418 - loss: 1.4441
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4419 - loss: 1.4439
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4420 - loss: 1.4436 - val_accuracy: 0.4555 - val_loss: 1.3703
Epoch 17/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4453 - loss: 1.3394
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4529 - loss: 1.3972 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4477 - loss: 1.4164
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4477 - loss: 1.4173
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4469 - loss: 1.4187
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4466 - loss: 1.4187
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4465 - loss: 1.4179
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4465 - loss: 1.4176 - val_accuracy: 0.4565 - val_loss: 1.3613
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4297 - loss: 1.4506
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4590 - loss: 1.3891 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4622 - loss: 1.3867
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4610 - loss: 1.3884
[1m 77/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4600 - loss: 1.3898
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4598 - loss: 1.3901
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4600 - loss: 1.3896
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4599 - loss: 1.3898
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4599 - loss: 1.3898 - val_accuracy: 0.4488 - val_loss: 1.3803

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:17[0m 1s/step
[1m52/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 995us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/step
Saved model to disk.

Accuracy capturado en la ejecución 8: 47.83 [%]
F1-score capturado en la ejecución 8: 46.56 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:17[0m 1s/step
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[1m190/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 799us/step
[1m255/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 792us/step
[1m316/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 799us/step
[1m382/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 795us/step
[1m450/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 787us/step
[1m514/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 787us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 922us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 61/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 839us/step
[1m127/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 801us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 44.88 [%]
Global F1 score (validation) = 42.54 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.3553935e-04 7.4504392e-04 2.2170178e-04 ... 1.7649801e-04
  2.8359605e-04 5.2889598e-05]
 [1.5496500e-03 2.3919723e-03 1.2403561e-03 ... 5.7511695e-04
  7.6846173e-04 1.8776774e-04]
 [5.5921928e-04 7.4243260e-04 3.7050259e-04 ... 3.5448335e-04
  3.4417654e-04 7.9428828e-05]
 ...
 [8.2653305e-06 7.8655985e-06 8.1163007e-06 ... 1.8472166e-03
  3.5438544e-04 1.4295675e-03]
 [1.0092459e-05 8.5920792e-06 1.0475598e-05 ... 2.4011058e-03
  3.8692832e-04 2.2635949e-03]
 [4.9629511e-04 4.5393780e-04 6.7622541e-04 ... 2.7302834e-01
  1.1469735e-03 7.5905309e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 53.85 [%]
Global accuracy score (test) = 44.4 [%]
Global F1 score (train) = 51.49 [%]
Global F1 score (test) = 42.08 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.14      0.17       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.32      0.26       161
       CAMINAR USUAL SPEED       0.28      0.40      0.33       161
            CAMINAR ZIGZAG       0.19      0.06      0.09       161
          DE PIE BARRIENDO       0.37      0.34      0.35       161
   DE PIE DOBLANDO TOALLAS       0.17      0.02      0.04       161
    DE PIE MOVIENDO LIBROS       0.30      0.63      0.41       161
          DE PIE USANDO PC       0.76      0.82      0.79       161
        FASE REPOSO CON K5       0.50      0.87      0.63       161
INCREMENTAL CICLOERGOMETRO       0.97      0.91      0.94       161
           SENTADO LEYENDO       0.40      0.35      0.37       161
         SENTADO USANDO PC       0.39      0.34      0.36       161
      SENTADO VIENDO LA TV       0.27      0.11      0.16       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.64      0.58       161
                    TROTAR       0.91      0.76      0.83       138

                  accuracy                           0.44      2392
                 macro avg       0.43      0.45      0.42      2392
              weighted avg       0.43      0.44      0.42      2392

2025-10-27 12:31:59.471901: 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`.
2025-10-27 12:31:59.484666: 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 00:00:1761564719.498460  689433 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564719.502777  689433 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564719.513935  689433 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564719.513968  689433 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564719.513971  689433 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564719.513974  689433 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:31:59.517504: 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 00:00:1761564721.927471  689433 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564724.506849  689576 service.cc:152] XLA service 0x78ed60013110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564724.506919  689576 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:32:04.564828: 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 00:00:1761564724.860244  689576 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564728.765051  689576 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:47[0m 6s/step - accuracy: 0.0391 - loss: 3.1902
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0746 - loss: 3.0924  
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0857 - loss: 3.0404
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0933 - loss: 3.0042
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0994 - loss: 2.9753
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1042 - loss: 2.9530
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1086 - loss: 2.93072025-10-27 12:32:10.513834: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:32:13.321354: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1112 - loss: 2.91712025-10-27 12:32:15.322186: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 53ms/step - accuracy: 0.1114 - loss: 2.9162 - val_accuracy: 0.2168 - val_loss: 2.1915
Epoch 2/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1627 - loss: 2.5849 
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[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1640 - loss: 2.5652
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1662 - loss: 2.5561
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1684 - loss: 2.5473
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Epoch 3/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2214 - loss: 2.2839
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2225 - loss: 2.2807
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Epoch 4/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2489 - loss: 2.1169 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2537 - loss: 2.1186
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2557 - loss: 2.1133
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2564 - loss: 2.1094
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2572 - loss: 2.1059
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2580 - loss: 2.1025 - val_accuracy: 0.3470 - val_loss: 1.6090
Epoch 5/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2521 - loss: 2.0409 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2623 - loss: 2.0217
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2671 - loss: 2.0142
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2709 - loss: 2.0063
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2738 - loss: 1.9992
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2764 - loss: 1.9932
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2786 - loss: 1.9882 - val_accuracy: 0.3678 - val_loss: 1.5860
Epoch 6/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3121 - loss: 1.8770 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3064 - loss: 1.8815
[1m 64/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3058 - loss: 1.8833
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3075 - loss: 1.8819
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3090 - loss: 1.8797
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.3104 - loss: 1.8772
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3114 - loss: 1.8755 - val_accuracy: 0.3937 - val_loss: 1.5167
Epoch 7/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3241 - loss: 1.8270 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3227 - loss: 1.8301
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3234 - loss: 1.8254
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3243 - loss: 1.8229
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3250 - loss: 1.8207
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3254 - loss: 1.8193
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3263 - loss: 1.8174
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Epoch 8/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3654 - loss: 1.7423 
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[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3536 - loss: 1.7422
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Epoch 9/92

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Epoch 10/92

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Epoch 11/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3886 - loss: 1.5919 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3976 - loss: 1.6001
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3987 - loss: 1.5992
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3993 - loss: 1.5995
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3997 - loss: 1.5995 - val_accuracy: 0.4397 - val_loss: 1.3771
Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3998 - loss: 1.5732 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4033 - loss: 1.5637
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4037 - loss: 1.5626
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4041 - loss: 1.5613
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4047 - loss: 1.5599 - val_accuracy: 0.4526 - val_loss: 1.3597
Epoch 13/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4151 - loss: 1.5698 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4164 - loss: 1.5466
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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4175 - loss: 1.5398
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Epoch 14/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4415 - loss: 1.4624 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4307 - loss: 1.4872
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4302 - loss: 1.4872
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4302 - loss: 1.4871
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4299 - loss: 1.4871 - val_accuracy: 0.4494 - val_loss: 1.3875
Epoch 15/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4219 - loss: 1.4049
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4355 - loss: 1.4593
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Epoch 16/92

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[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4400 - loss: 1.4452
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4407 - loss: 1.4440 - val_accuracy: 0.4466 - val_loss: 1.3878
Epoch 17/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.3636
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4505 - loss: 1.4200 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4518 - loss: 1.4159
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[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4519 - loss: 1.4095
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4514 - loss: 1.4096
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 1s/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 899us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 9: 44.4 [%]
F1-score capturado en la ejecución 9: 42.08 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m192/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 794us/step
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 952us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 61/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 845us/step
[1m124/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 823us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 45.47 [%]
Global F1 score (validation) = 43.48 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.1525511e-04 1.0796866e-03 2.2296725e-04 ... 7.5798307e-05
  5.9493555e-04 8.6877830e-05]
 [3.7749945e-03 9.1219367e-03 3.1254687e-03 ... 7.1958074e-04
  4.8559336e-03 4.4790239e-04]
 [8.6353376e-04 1.9742106e-03 6.4525520e-04 ... 3.4510216e-04
  1.6417815e-03 1.0911879e-04]
 ...
 [3.4345321e-05 1.1389334e-05 1.6559157e-05 ... 3.6779724e-03
  5.4340664e-04 1.2243001e-03]
 [2.2150094e-05 7.4153909e-06 1.0186862e-05 ... 2.0863477e-03
  4.0062613e-04 5.4973527e-04]
 [1.1045611e-03 4.2633791e-04 6.8751496e-04 ... 1.3460337e-01
  3.2840024e-03 2.3207895e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 54.64 [%]
Global accuracy score (test) = 44.9 [%]
Global F1 score (train) = 52.97 [%]
Global F1 score (test) = 43.28 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.06      0.09       161
 CAMINAR CON MÓVIL O LIBRO       0.20      0.17      0.18       161
       CAMINAR USUAL SPEED       0.30      0.44      0.36       161
            CAMINAR ZIGZAG       0.19      0.22      0.20       161
          DE PIE BARRIENDO       0.39      0.48      0.43       161
   DE PIE DOBLANDO TOALLAS       0.29      0.16      0.20       161
    DE PIE MOVIENDO LIBROS       0.34      0.39      0.36       161
          DE PIE USANDO PC       0.70      0.84      0.76       161
        FASE REPOSO CON K5       0.51      0.87      0.64       161
INCREMENTAL CICLOERGOMETRO       0.97      0.95      0.96       161
           SENTADO LEYENDO       0.34      0.29      0.31       161
         SENTADO USANDO PC       0.57      0.35      0.43       161
      SENTADO VIENDO LA TV       0.19      0.14      0.16       161
   SUBIR Y BAJAR ESCALERAS       0.48      0.68      0.57       161
                    TROTAR       0.92      0.75      0.82       138

                  accuracy                           0.45      2392
                 macro avg       0.44      0.45      0.43      2392
              weighted avg       0.43      0.45      0.43      2392

2025-10-27 12:32:38.735348: 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`.
2025-10-27 12:32:38.748052: 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 00:00:1761564758.762241  692461 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564758.766716  692461 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564758.777938  692461 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564758.777970  692461 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564758.777973  692461 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564758.777991  692461 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:32:38.781439: 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 00:00:1761564761.233440  692461 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564763.842176  692627 service.cc:152] XLA service 0x785d10018b10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564763.842253  692627 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:32:43.898457: 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 00:00:1761564764.217023  692627 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564768.142407  692627 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:53[0m 6s/step - accuracy: 0.0703 - loss: 3.0794
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0926 - loss: 3.0615  
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0978 - loss: 3.0223
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1097 - loss: 2.9566
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1140 - loss: 2.9316
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1174 - loss: 2.91162025-10-27 12:32:50.148735: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:32:52.777723: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1197 - loss: 2.89782025-10-27 12:32:54.914490: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1198 - loss: 2.8969 - val_accuracy: 0.2356 - val_loss: 2.1066
Epoch 2/92

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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1769 - loss: 2.5395
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1794 - loss: 2.5196 - val_accuracy: 0.2834 - val_loss: 1.8938
Epoch 3/92

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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2255 - loss: 2.2893
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2264 - loss: 2.2827
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Epoch 4/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2535 - loss: 2.1303
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Epoch 5/92

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[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2880 - loss: 1.9865
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 1.9839
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Epoch 6/92

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[1m 24/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2994 - loss: 1.9790 
[1m 47/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2992 - loss: 1.9555
[1m 71/137[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3003 - loss: 1.9388
[1m 94/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3014 - loss: 1.9277
[1m113/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3025 - loss: 1.9193
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 1.9105
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3040 - loss: 1.9094 - val_accuracy: 0.3897 - val_loss: 1.4750
Epoch 7/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3153 - loss: 1.8562 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3163 - loss: 1.8474
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3184 - loss: 1.8408
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3199 - loss: 1.8384
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[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3222 - loss: 1.8319
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3229 - loss: 1.8296 - val_accuracy: 0.4229 - val_loss: 1.4316
Epoch 8/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3381 - loss: 1.7629 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3367 - loss: 1.7653
[1m 67/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3384 - loss: 1.7636
[1m 89/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3394 - loss: 1.7639
[1m110/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3405 - loss: 1.7626
[1m131/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3412 - loss: 1.7616
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Epoch 9/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3638 - loss: 1.7142 
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[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3669 - loss: 1.6982
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Epoch 10/92

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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3816 - loss: 1.6513
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Epoch 11/92

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Epoch 12/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4007 - loss: 1.6164 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4001 - loss: 1.6075
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4010 - loss: 1.5982
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4031 - loss: 1.5875 - val_accuracy: 0.4520 - val_loss: 1.3659
Epoch 13/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4143 - loss: 1.5376 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4116 - loss: 1.5365
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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4117 - loss: 1.5336
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4121 - loss: 1.5323
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4123 - loss: 1.5318
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4125 - loss: 1.5318 - val_accuracy: 0.4538 - val_loss: 1.3596
Epoch 14/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4276 - loss: 1.4974 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4293 - loss: 1.4866
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4292 - loss: 1.4869
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[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4289 - loss: 1.4898
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Epoch 15/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4524 - loss: 1.4270 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4450 - loss: 1.4478
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4428 - loss: 1.4513
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4415 - loss: 1.4539
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4408 - loss: 1.4555 - val_accuracy: 0.4545 - val_loss: 1.3691
Epoch 16/92

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[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4482 - loss: 1.4496 
[1m 37/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4511 - loss: 1.4510
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4520 - loss: 1.4492
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4513 - loss: 1.4487
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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4495 - loss: 1.4464
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Epoch 17/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4464 - loss: 1.4154 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4422 - loss: 1.4219
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4428 - loss: 1.4196
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4423 - loss: 1.4205
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4424 - loss: 1.4212
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4427 - loss: 1.4212 - val_accuracy: 0.4674 - val_loss: 1.3524
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.4304
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4531 - loss: 1.4062 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4492 - loss: 1.4168
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4496 - loss: 1.4146
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4510 - loss: 1.4099
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4519 - loss: 1.4072
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4526 - loss: 1.4051 - val_accuracy: 0.4587 - val_loss: 1.3824
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3984 - loss: 1.3990
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4486 - loss: 1.3937 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4498 - loss: 1.3928
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4509 - loss: 1.3896
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4521 - loss: 1.3864
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4535 - loss: 1.3834
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4548 - loss: 1.3810
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4558 - loss: 1.3794 - val_accuracy: 0.4599 - val_loss: 1.3790
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4531 - loss: 1.3215
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4792 - loss: 1.3210 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4807 - loss: 1.3234
[1m 64/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4794 - loss: 1.3286
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4779 - loss: 1.3332
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4769 - loss: 1.3375
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4762 - loss: 1.3404
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4758 - loss: 1.3420 - val_accuracy: 0.4619 - val_loss: 1.3776
Epoch 21/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4847 - loss: 1.3426 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4857 - loss: 1.3360
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4851 - loss: 1.3332
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Epoch 22/92

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[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 863us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 10: 44.9 [%]
F1-score capturado en la ejecución 10: 43.28 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 54/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 953us/step
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[1m483/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 841us/step
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[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 858us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 857us/step
[1m121/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 841us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 47.47 [%]
Global F1 score (validation) = 45.94 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.5204811e-03 2.0957002e-03 1.3042415e-03 ... 6.0567114e-04
  1.0084907e-03 3.4893246e-04]
 [3.0121820e-03 3.9503747e-03 1.6504050e-03 ... 5.2179472e-04
  2.0719990e-03 5.9751055e-04]
 [4.1042801e-04 6.1667565e-04 2.4632926e-04 ... 3.0223001e-04
  8.4694143e-04 2.6810719e-04]
 ...
 [5.0489061e-06 3.5492201e-06 3.5467228e-06 ... 1.5024039e-03
  5.3224497e-04 9.9014456e-04]
 [9.0047515e-06 5.7583493e-06 6.8326090e-06 ... 3.5845360e-03
  4.4507047e-04 1.7185323e-03]
 [2.1249472e-04 1.6547101e-04 2.8520147e-04 ... 2.8825447e-01
  3.8133445e-04 3.5968791e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 58.93 [%]
Global accuracy score (test) = 44.48 [%]
Global F1 score (train) = 57.64 [%]
Global F1 score (test) = 43.35 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.37      0.29       161
 CAMINAR CON MÓVIL O LIBRO       0.20      0.14      0.16       161
       CAMINAR USUAL SPEED       0.21      0.14      0.17       161
            CAMINAR ZIGZAG       0.22      0.23      0.23       161
          DE PIE BARRIENDO       0.43      0.52      0.47       161
   DE PIE DOBLANDO TOALLAS       0.26      0.13      0.17       161
    DE PIE MOVIENDO LIBROS       0.36      0.51      0.42       161
          DE PIE USANDO PC       0.80      0.81      0.80       161
        FASE REPOSO CON K5       0.51      0.88      0.64       161
INCREMENTAL CICLOERGOMETRO       0.99      0.91      0.94       161
           SENTADO LEYENDO       0.38      0.29      0.33       161
         SENTADO USANDO PC       0.65      0.20      0.30       161
      SENTADO VIENDO LA TV       0.12      0.13      0.12       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.72      0.62       161
                    TROTAR       0.87      0.75      0.81       138

                  accuracy                           0.44      2392
                 macro avg       0.45      0.45      0.43      2392
              weighted avg       0.45      0.44      0.43      2392

2025-10-27 12:33:20.106525: 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`.
2025-10-27 12:33:20.119264: 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 00:00:1761564800.133169  695985 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564800.137543  695985 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564800.148514  695985 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564800.148538  695985 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564800.148541  695985 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564800.148543  695985 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:33:20.151944: 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 00:00:1761564802.554463  695985 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564805.140006  696138 service.cc:152] XLA service 0x7e3dfc0131a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564805.140088  696138 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:33:25.198777: 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 00:00:1761564805.498369  696138 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564809.471550  696138 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:55[0m 6s/step - accuracy: 0.1094 - loss: 3.0625
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[1m 96/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1053 - loss: 2.9521
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1096 - loss: 2.9278
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.1134 - loss: 2.90592025-10-27 12:33:31.265900: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:33:34.076269: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1136 - loss: 2.90482025-10-27 12:33:36.241109: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1137 - loss: 2.9038 - val_accuracy: 0.2660 - val_loss: 2.1097
Epoch 2/92

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[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1711 - loss: 2.5253
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1732 - loss: 2.5146
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1752 - loss: 2.5046 - val_accuracy: 0.2700 - val_loss: 1.8377
Epoch 3/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2095 - loss: 2.3022
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2125 - loss: 2.2941
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2146 - loss: 2.2858
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2166 - loss: 2.2781
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2183 - loss: 2.2711 - val_accuracy: 0.3215 - val_loss: 1.7324
Epoch 4/92

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[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2490 - loss: 2.1174
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2511 - loss: 2.1104
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2526 - loss: 2.1048 - val_accuracy: 0.3520 - val_loss: 1.6174
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 1.8648
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2847 - loss: 2.0140 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2817 - loss: 2.0145
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2793 - loss: 2.0071
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Epoch 6/92

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[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3094 - loss: 1.8974
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Epoch 7/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3409 - loss: 1.7975
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3396 - loss: 1.7977
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3386 - loss: 1.7979
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3378 - loss: 1.7976 - val_accuracy: 0.4077 - val_loss: 1.4814
Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3328 - loss: 1.7515 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3390 - loss: 1.7624
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3396 - loss: 1.7606
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3403 - loss: 1.7580
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Epoch 9/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3562 - loss: 1.7675 
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[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3570 - loss: 1.7419
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3565 - loss: 1.7356
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3564 - loss: 1.7312
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3571 - loss: 1.7262
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3580 - loss: 1.7219
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3581 - loss: 1.7214 - val_accuracy: 0.4148 - val_loss: 1.4049
Epoch 10/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3734 - loss: 1.6777 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3769 - loss: 1.6601
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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3775 - loss: 1.6569
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Epoch 11/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4019 - loss: 1.6137 
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3925 - loss: 1.6136
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3923 - loss: 1.6124
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Epoch 12/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4124 - loss: 1.5573
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Epoch 13/92

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[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4203 - loss: 1.5169
[1m133/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4201 - loss: 1.5176
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.4201 - loss: 1.5177 - val_accuracy: 0.4455 - val_loss: 1.3838
Epoch 14/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4302 - loss: 1.5218 
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4276 - loss: 1.5028
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4275 - loss: 1.5005
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4274 - loss: 1.4989
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4276 - loss: 1.4969
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4276 - loss: 1.4967 - val_accuracy: 0.4512 - val_loss: 1.3720
Epoch 15/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4766 - loss: 1.4772
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4522 - loss: 1.4383 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4452 - loss: 1.4458
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4404 - loss: 1.4534
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4395 - loss: 1.4547
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4388 - loss: 1.4558
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4384 - loss: 1.4561 - val_accuracy: 0.4466 - val_loss: 1.3839
Epoch 16/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4716 - loss: 1.3794 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4516 - loss: 1.4067
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Epoch 17/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4602 - loss: 1.4004
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4591 - loss: 1.4001
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Epoch 18/92

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[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4496 - loss: 1.4065
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Epoch 19/92

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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4653 - loss: 1.3757
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Epoch 20/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4687 - loss: 1.3502 
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[1m 96/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4727 - loss: 1.3509
[1m114/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4734 - loss: 1.3498
[1m133/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4737 - loss: 1.3490
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.4738 - loss: 1.3488 - val_accuracy: 0.4593 - val_loss: 1.3764
Epoch 21/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4960 - loss: 1.2954 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4935 - loss: 1.2996
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4914 - loss: 1.3034
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4901 - loss: 1.3063
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4893 - loss: 1.3080
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4885 - loss: 1.3094
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4877 - loss: 1.3107 - val_accuracy: 0.4613 - val_loss: 1.3938
Epoch 22/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.5060 - loss: 1.2426 
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[1m 57/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4982 - loss: 1.2700
[1m 77/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4963 - loss: 1.2780
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4953 - loss: 1.2831
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Epoch 23/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4939 - loss: 1.2728 
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:17[0m 1s/step
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 956us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 11: 44.48 [%]
F1-score capturado en la ejecución 11: 43.35 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 54/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 952us/step
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[1m177/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 859us/step
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[1m483/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 839us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 844us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 58/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 880us/step
[1m112/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 906us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 45.51 [%]
Global F1 score (validation) = 42.92 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.3582835e-04 8.7167165e-04 3.5018372e-04 ... 1.1778824e-04
  3.4254557e-04 1.7559074e-04]
 [9.5696189e-04 1.8028319e-03 1.0460174e-03 ... 6.1237876e-04
  9.5877610e-04 2.5573457e-04]
 [7.0419611e-04 1.4331472e-03 7.2821352e-04 ... 4.7217181e-04
  7.8302837e-04 2.0092529e-04]
 ...
 [5.8385099e-06 2.5321515e-06 3.7364821e-06 ... 1.1920980e-03
  4.2750788e-04 4.0307702e-04]
 [6.8981772e-06 2.8565614e-06 4.3236209e-06 ... 1.5811891e-03
  4.8713107e-04 4.6016058e-04]
 [5.7915342e-04 2.3110143e-04 4.6775845e-04 ... 1.8216901e-01
  2.0479625e-03 2.6761261e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 57.89 [%]
Global accuracy score (test) = 42.35 [%]
Global F1 score (train) = 54.76 [%]
Global F1 score (test) = 39.27 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.16      0.17       161
 CAMINAR CON MÓVIL O LIBRO       0.19      0.16      0.17       161
       CAMINAR USUAL SPEED       0.28      0.39      0.32       161
            CAMINAR ZIGZAG       0.09      0.07      0.08       161
          DE PIE BARRIENDO       0.43      0.39      0.41       161
   DE PIE DOBLANDO TOALLAS       0.24      0.12      0.16       161
    DE PIE MOVIENDO LIBROS       0.32      0.48      0.38       161
          DE PIE USANDO PC       0.75      0.84      0.79       161
        FASE REPOSO CON K5       0.55      0.86      0.67       161
INCREMENTAL CICLOERGOMETRO       0.94      0.93      0.93       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.50      0.02      0.05       161
      SENTADO VIENDO LA TV       0.25      0.56      0.34       161
   SUBIR Y BAJAR ESCALERAS       0.51      0.69      0.59       161
                    TROTAR       0.94      0.74      0.83       138

                  accuracy                           0.42      2392
                 macro avg       0.41      0.43      0.39      2392
              weighted avg       0.41      0.42      0.39      2392

2025-10-27 12:34:02.237094: 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`.
2025-10-27 12:34:02.249872: 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 00:00:1761564842.263655  699613 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564842.267988  699613 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564842.279537  699613 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564842.279563  699613 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564842.279567  699613 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564842.279570  699613 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:34:02.283107: 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 00:00:1761564844.706033  699613 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564847.340920  699757 service.cc:152] XLA service 0x7932dc002860 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564847.340976  699757 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:34:07.396125: 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 00:00:1761564847.701723  699757 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564851.710810  699757 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:05[0m 6s/step - accuracy: 0.1016 - loss: 3.1887
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1079 - loss: 2.9816
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1122 - loss: 2.9505
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1156 - loss: 2.92592025-10-27 12:34:13.732402: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:34:16.379128: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 35ms/step - accuracy: 0.1186 - loss: 2.90352025-10-27 12:34:18.538216: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 54ms/step - accuracy: 0.1188 - loss: 2.9025 - val_accuracy: 0.2123 - val_loss: 2.1589
Epoch 2/92

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[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1703 - loss: 2.4996
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1726 - loss: 2.4897
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1745 - loss: 2.4813 - val_accuracy: 0.3049 - val_loss: 1.8529
Epoch 3/92

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[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2247 - loss: 2.2683
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2264 - loss: 2.2604
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2277 - loss: 2.2543
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2292 - loss: 2.2475
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2303 - loss: 2.2422 - val_accuracy: 0.3190 - val_loss: 1.7428
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.1504
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2676 - loss: 2.0730 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2634 - loss: 2.0796
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2611 - loss: 2.0786
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2606 - loss: 2.0768
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2604 - loss: 2.0747
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2605 - loss: 2.0721 - val_accuracy: 0.3431 - val_loss: 1.6386
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.2422 - loss: 2.0511
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2844 - loss: 1.9814 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2891 - loss: 1.9723
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2896 - loss: 1.9686
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2899 - loss: 1.9657
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2900 - loss: 1.9633 - val_accuracy: 0.3747 - val_loss: 1.5415
Epoch 6/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3084 - loss: 1.8813
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3078 - loss: 1.8815
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Epoch 7/92

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Epoch 8/92

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Epoch 9/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3604 - loss: 1.7397 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3617 - loss: 1.7127
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3616 - loss: 1.7115
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3616 - loss: 1.7102
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3618 - loss: 1.7086
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3619 - loss: 1.7082 - val_accuracy: 0.4326 - val_loss: 1.4114
Epoch 10/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3894 - loss: 1.6483 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3824 - loss: 1.6530
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3805 - loss: 1.6511
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3804 - loss: 1.6498
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3809 - loss: 1.6477
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3816 - loss: 1.6459
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3817 - loss: 1.6458 - val_accuracy: 0.4306 - val_loss: 1.4203
Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3845 - loss: 1.6182 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3881 - loss: 1.6147
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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3880 - loss: 1.6130
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Epoch 12/92

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[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3943 - loss: 1.5921
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3955 - loss: 1.5853
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3969 - loss: 1.5790
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3982 - loss: 1.5745
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Epoch 13/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4166 - loss: 1.5073 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4194 - loss: 1.5152
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4184 - loss: 1.5238
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Epoch 14/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4221 - loss: 1.5036
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4216 - loss: 1.5048
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4216 - loss: 1.5051
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Epoch 15/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4293 - loss: 1.4679 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4296 - loss: 1.4745
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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4306 - loss: 1.4721
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4303 - loss: 1.4712
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4301 - loss: 1.4707
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4300 - loss: 1.4701 - val_accuracy: 0.4553 - val_loss: 1.3836
Epoch 16/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4260 - loss: 1.4865 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4291 - loss: 1.4763
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4310 - loss: 1.4671
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4324 - loss: 1.4605
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4340 - loss: 1.4563
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4352 - loss: 1.4532
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4358 - loss: 1.4518 - val_accuracy: 0.4543 - val_loss: 1.3713
Epoch 17/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4555 - loss: 1.4162 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4579 - loss: 1.4096
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4562 - loss: 1.4106
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Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4699 - loss: 1.3404 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4653 - loss: 1.3590
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4639 - loss: 1.3651
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4630 - loss: 1.3682
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4628 - loss: 1.3706
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4627 - loss: 1.3727
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4622 - loss: 1.3744 - val_accuracy: 0.4692 - val_loss: 1.3663
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5078 - loss: 1.3295
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4935 - loss: 1.3508 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4868 - loss: 1.3613
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4821 - loss: 1.3647
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4792 - loss: 1.3661
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4775 - loss: 1.3658
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4768 - loss: 1.3651
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4763 - loss: 1.3648 - val_accuracy: 0.4628 - val_loss: 1.3813
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4375 - loss: 1.2891
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4736 - loss: 1.3499 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4726 - loss: 1.3519
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4713 - loss: 1.3541
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4717 - loss: 1.3524
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4718 - loss: 1.3513
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4717 - loss: 1.3510
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4715 - loss: 1.3510 - val_accuracy: 0.4660 - val_loss: 1.3821
Epoch 21/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4922 - loss: 1.3143
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4636 - loss: 1.3556 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4627 - loss: 1.3522
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4632 - loss: 1.3501
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4639 - loss: 1.3473
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4645 - loss: 1.3452
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4652 - loss: 1.3436
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4658 - loss: 1.3426 - val_accuracy: 0.4557 - val_loss: 1.4177
Epoch 22/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4531 - loss: 1.4190
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4566 - loss: 1.3518 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4599 - loss: 1.3416
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4631 - loss: 1.3344
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4655 - loss: 1.3298
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4680 - loss: 1.3265
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4698 - loss: 1.3244
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4712 - loss: 1.3225 - val_accuracy: 0.4757 - val_loss: 1.3948
Epoch 23/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.3744
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4849 - loss: 1.3085 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4884 - loss: 1.3070
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4903 - loss: 1.3045
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4918 - loss: 1.3017
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4925 - loss: 1.3006
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4930 - loss: 1.2998
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4933 - loss: 1.2991 - val_accuracy: 0.4709 - val_loss: 1.4138

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 1s/step
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 12: 42.35 [%]
F1-score capturado en la ejecución 12: 39.27 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:25[0m 1s/step
[1m 58/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 885us/step
[1m122/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 832us/step
[1m190/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 800us/step
[1m247/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 819us/step
[1m311/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 813us/step
[1m372/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 815us/step
[1m429/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 826us/step
[1m493/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 820us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 903us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 61/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 844us/step
[1m124/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 821us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 47.09 [%]
Global F1 score (validation) = 45.03 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.8762975e-04 1.6451755e-03 6.8487751e-04 ... 2.6873263e-04
  6.8552542e-04 4.1823857e-04]
 [1.5547579e-03 3.0338117e-03 1.7052145e-03 ... 8.7650568e-04
  1.8036498e-03 4.8168839e-04]
 [9.9276984e-04 1.7487053e-03 1.1885283e-03 ... 6.7139877e-04
  1.0547068e-03 3.9216122e-04]
 ...
 [5.2206292e-06 7.5733815e-06 4.8057605e-06 ... 8.8086538e-04
  5.7111628e-04 1.0979789e-03]
 [6.1680980e-06 8.4149815e-06 6.4576907e-06 ... 1.5652769e-03
  5.2392995e-04 1.6744259e-03]
 [4.9534312e-04 2.7697548e-04 6.5951189e-04 ... 2.9140949e-01
  1.7573250e-03 2.6029132e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 57.98 [%]
Global accuracy score (test) = 46.11 [%]
Global F1 score (train) = 55.9 [%]
Global F1 score (test) = 44.27 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.20      0.21       161
 CAMINAR CON MÓVIL O LIBRO       0.23      0.25      0.24       161
       CAMINAR USUAL SPEED       0.23      0.40      0.29       161
            CAMINAR ZIGZAG       0.25      0.08      0.12       161
          DE PIE BARRIENDO       0.48      0.52      0.50       161
   DE PIE DOBLANDO TOALLAS       0.39      0.25      0.30       161
    DE PIE MOVIENDO LIBROS       0.34      0.40      0.37       161
          DE PIE USANDO PC       0.73      0.86      0.79       161
        FASE REPOSO CON K5       0.53      0.88      0.66       161
INCREMENTAL CICLOERGOMETRO       0.98      0.93      0.96       161
           SENTADO LEYENDO       0.40      0.57      0.47       161
         SENTADO USANDO PC       0.34      0.08      0.13       161
      SENTADO VIENDO LA TV       0.24      0.13      0.17       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.65      0.58       161
                    TROTAR       0.95      0.77      0.85       138

                  accuracy                           0.46      2392
                 macro avg       0.46      0.46      0.44      2392
              weighted avg       0.45      0.46      0.44      2392

2025-10-27 12:34:44.542209: 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`.
2025-10-27 12:34:44.554598: 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 00:00:1761564884.568182  703220 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564884.572340  703220 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564884.583367  703220 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564884.583391  703220 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564884.583395  703220 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564884.583399  703220 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:34:44.586633: 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 00:00:1761564886.959372  703220 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564889.510050  703375 service.cc:152] XLA service 0x726248003a20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564889.510106  703375 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:34:49.564239: 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 00:00:1761564889.867409  703375 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564893.732913  703375 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:36[0m 6s/step - accuracy: 0.0469 - loss: 3.1204
[1m 17/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0755 - loss: 3.0907  
[1m 36/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0890 - loss: 3.0290
[1m 56/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0973 - loss: 2.9912
[1m 75/137[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1037 - loss: 2.9605
[1m 93/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1083 - loss: 2.9369
[1m114/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1128 - loss: 2.9141
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.1162 - loss: 2.89432025-10-27 12:34:55.445799: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:34:58.350719: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1165 - loss: 2.89252025-10-27 12:35:00.437190: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1167 - loss: 2.8916 - val_accuracy: 0.2091 - val_loss: 2.1413
Epoch 2/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 38ms/step - accuracy: 0.1484 - loss: 2.6142
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1751 - loss: 2.5345 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1741 - loss: 2.5299
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1744 - loss: 2.5236
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1758 - loss: 2.5151
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1773 - loss: 2.5051
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1789 - loss: 2.4960
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 3ms/step - accuracy: 0.1802 - loss: 2.4891 - val_accuracy: 0.2773 - val_loss: 1.8501
Epoch 3/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1875 - loss: 2.4585
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2135 - loss: 2.3295 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2177 - loss: 2.3080
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2199 - loss: 2.2937
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2211 - loss: 2.2853
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2221 - loss: 2.2792
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2228 - loss: 2.2740
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2236 - loss: 2.2692
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2237 - loss: 2.2690 - val_accuracy: 0.3198 - val_loss: 1.7268
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.0120
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2600 - loss: 2.1133 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2595 - loss: 2.1170
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2585 - loss: 2.1174
[1m 76/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2589 - loss: 2.1160
[1m 95/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2589 - loss: 2.1149
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2589 - loss: 2.1130
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.2592 - loss: 2.1101
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2593 - loss: 2.1098 - val_accuracy: 0.3532 - val_loss: 1.6194
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3203 - loss: 1.9681
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2834 - loss: 2.0101 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2816 - loss: 2.0014
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2809 - loss: 1.9961
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2811 - loss: 1.9912
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2819 - loss: 1.9856
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2826 - loss: 1.9820
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2834 - loss: 1.9794 - val_accuracy: 0.3753 - val_loss: 1.5396
Epoch 6/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 1.9236
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3035 - loss: 1.8951 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2991 - loss: 1.9018
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2987 - loss: 1.9028
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2986 - loss: 1.9027
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2999 - loss: 1.9006
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3012 - loss: 1.8981
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3023 - loss: 1.8961
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3024 - loss: 1.8960 - val_accuracy: 0.3808 - val_loss: 1.5080
Epoch 7/92

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Epoch 8/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3293 - loss: 1.7934
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Epoch 9/92

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Epoch 10/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3921 - loss: 1.6731 
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3864 - loss: 1.6607
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3848 - loss: 1.6540 - val_accuracy: 0.4387 - val_loss: 1.3706
Epoch 11/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3803 - loss: 1.6165 
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[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3897 - loss: 1.5980
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3908 - loss: 1.5956
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3922 - loss: 1.5934
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3931 - loss: 1.5922
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3935 - loss: 1.5918
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3935 - loss: 1.5917 - val_accuracy: 0.4453 - val_loss: 1.3649
Epoch 12/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4018 - loss: 1.5769 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3999 - loss: 1.5771
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4020 - loss: 1.5680
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Epoch 13/92

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Epoch 14/92

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[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4335 - loss: 1.4945
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Epoch 15/92

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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4312 - loss: 1.4661
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Epoch 16/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4472 - loss: 1.4236 
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4426 - loss: 1.4307 - val_accuracy: 0.4690 - val_loss: 1.3245
Epoch 17/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4610 - loss: 1.3914 
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4511 - loss: 1.4134
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Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4710 - loss: 1.3673 
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[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4568 - loss: 1.3916
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Epoch 19/92

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Epoch 20/92

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Epoch 21/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4737 - loss: 1.3360 
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 1s/step
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 848us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 13: 46.11 [%]
F1-score capturado en la ejecución 13: 44.27 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m118/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 865us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 46.88 [%]
Global F1 score (validation) = 44.53 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[6.4838713e-04 1.1856037e-03 4.7440431e-04 ... 4.0302795e-04
  1.0836753e-03 2.5595317e-04]
 [2.4436195e-03 4.6098372e-03 2.4550173e-03 ... 1.1597272e-03
  2.3071982e-03 4.7876130e-04]
 [5.8596104e-04 1.2301953e-03 5.4776855e-04 ... 6.8419700e-04
  9.0482848e-04 1.5624121e-04]
 ...
 [9.7350503e-06 5.4813481e-06 5.6387244e-06 ... 9.6037769e-04
  3.9372343e-04 3.0884880e-04]
 [9.2724713e-06 5.0112621e-06 5.7603247e-06 ... 9.9646684e-04
  2.8646784e-04 6.3880219e-04]
 [8.8651304e-04 4.2392674e-04 7.2513038e-04 ... 3.2638854e-01
  9.8340015e-04 1.2791783e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.97 [%]
Global accuracy score (test) = 44.86 [%]
Global F1 score (train) = 55.14 [%]
Global F1 score (test) = 42.75 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.16      0.18       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.27      0.26       161
       CAMINAR USUAL SPEED       0.25      0.25      0.25       161
            CAMINAR ZIGZAG       0.25      0.22      0.24       161
          DE PIE BARRIENDO       0.44      0.55      0.49       161
   DE PIE DOBLANDO TOALLAS       0.36      0.19      0.25       161
    DE PIE MOVIENDO LIBROS       0.36      0.48      0.41       161
          DE PIE USANDO PC       0.78      0.83      0.80       161
        FASE REPOSO CON K5       0.48      0.88      0.62       161
INCREMENTAL CICLOERGOMETRO       0.95      0.92      0.93       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.36      0.38      0.37       161
      SENTADO VIENDO LA TV       0.18      0.17      0.18       161
   SUBIR Y BAJAR ESCALERAS       0.51      0.72      0.59       161
                    TROTAR       0.94      0.76      0.84       138

                  accuracy                           0.45      2392
                 macro avg       0.42      0.45      0.43      2392
              weighted avg       0.42      0.45      0.42      2392

2025-10-27 12:35:25.793914: 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`.
2025-10-27 12:35:25.806652: 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 00:00:1761564925.820777  706657 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564925.825257  706657 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564925.836507  706657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564925.836529  706657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564925.836532  706657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564925.836535  706657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:35:25.840002: 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 00:00:1761564928.250278  706657 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564930.793833  706806 service.cc:152] XLA service 0x7dbbac0121c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564930.793904  706806 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:35:30.853988: 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 00:00:1761564931.149793  706806 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564935.142589  706806 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:53[0m 6s/step - accuracy: 0.0312 - loss: 3.4099
[1m 18/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0593 - loss: 3.1559  
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0773 - loss: 3.0735
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0961 - loss: 2.9906
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1017 - loss: 2.9634
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1060 - loss: 2.94122025-10-27 12:35:37.121675: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:35:39.750783: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1097 - loss: 2.92112025-10-27 12:35:41.873420: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1099 - loss: 2.9201 - val_accuracy: 0.2087 - val_loss: 2.2088
Epoch 2/92

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[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1737 - loss: 2.5303
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1755 - loss: 2.5196
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1772 - loss: 2.5096 - val_accuracy: 0.2870 - val_loss: 1.9503
Epoch 3/92

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[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2158 - loss: 2.2788
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2187 - loss: 2.2708
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2206 - loss: 2.2649 - val_accuracy: 0.3079 - val_loss: 1.7347
Epoch 4/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2398 - loss: 2.1471
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2455 - loss: 2.1304
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Epoch 5/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2781 - loss: 1.9944
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Epoch 6/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3195 - loss: 1.8936 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3111 - loss: 1.8945
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3102 - loss: 1.8892 - val_accuracy: 0.3796 - val_loss: 1.5344
Epoch 7/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3250 - loss: 1.8524 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 1.8411
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3230 - loss: 1.8336
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[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3246 - loss: 1.8269
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3250 - loss: 1.8251 - val_accuracy: 0.3939 - val_loss: 1.4611
Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3300 - loss: 1.7937 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3355 - loss: 1.7877
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3366 - loss: 1.7824
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3374 - loss: 1.7791
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3384 - loss: 1.7757
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3393 - loss: 1.7729
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3394 - loss: 1.7726 - val_accuracy: 0.4243 - val_loss: 1.4214
Epoch 9/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3487 - loss: 1.7827 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3570 - loss: 1.7369
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Epoch 10/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3742 - loss: 1.6543
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Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3616 - loss: 1.6267 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3759 - loss: 1.6225
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[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3800 - loss: 1.6186
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.3815 - loss: 1.6167 - val_accuracy: 0.4417 - val_loss: 1.3773
Epoch 12/92

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Epoch 13/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4095 - loss: 1.5237 
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[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4133 - loss: 1.5315
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4135 - loss: 1.5317
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4136 - loss: 1.5318
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4137 - loss: 1.5318 - val_accuracy: 0.4488 - val_loss: 1.3548
Epoch 14/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4114 - loss: 1.5094 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4176 - loss: 1.5159
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4190 - loss: 1.5159
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4202 - loss: 1.5147
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4209 - loss: 1.5133 - val_accuracy: 0.4682 - val_loss: 1.3390
Epoch 15/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4363 - loss: 1.4697 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4313 - loss: 1.4697
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4307 - loss: 1.4710 - val_accuracy: 0.4593 - val_loss: 1.3460
Epoch 16/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 28ms/step - accuracy: 0.5156 - loss: 1.4063
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4564 - loss: 1.4404 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4497 - loss: 1.4483
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4464 - loss: 1.4492
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4454 - loss: 1.4488
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4447 - loss: 1.4478
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4446 - loss: 1.4459
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4445 - loss: 1.4445 - val_accuracy: 0.4660 - val_loss: 1.3334
Epoch 17/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3906 - loss: 1.4612
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4254 - loss: 1.4630 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4386 - loss: 1.4485
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4428 - loss: 1.4408
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4443 - loss: 1.4374
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4454 - loss: 1.4341
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4462 - loss: 1.4319
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4468 - loss: 1.4305 - val_accuracy: 0.4670 - val_loss: 1.3508
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4453 - loss: 1.5145
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4455 - loss: 1.4280 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4497 - loss: 1.4145
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4524 - loss: 1.4080
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4538 - loss: 1.4044
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4542 - loss: 1.4034
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4547 - loss: 1.4024
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4553 - loss: 1.4013 - val_accuracy: 0.4625 - val_loss: 1.3477
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5312 - loss: 1.2855
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4638 - loss: 1.3431 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4638 - loss: 1.3522
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4661 - loss: 1.3543
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4672 - loss: 1.3550
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4676 - loss: 1.3561
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4675 - loss: 1.3580
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4674 - loss: 1.3597 - val_accuracy: 0.4729 - val_loss: 1.3524
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4766 - loss: 1.2984
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4850 - loss: 1.3211 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4811 - loss: 1.3408
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4800 - loss: 1.3457
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4795 - loss: 1.3482
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4794 - loss: 1.3485
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4795 - loss: 1.3485
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4794 - loss: 1.3484 - val_accuracy: 0.4709 - val_loss: 1.3553
Epoch 21/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4609 - loss: 1.2716
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4729 - loss: 1.3159 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4790 - loss: 1.3173
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4800 - loss: 1.3218
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4800 - loss: 1.3256
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4803 - loss: 1.3273
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4807 - loss: 1.3284
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4809 - loss: 1.3289 - val_accuracy: 0.4640 - val_loss: 1.3858

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 1s/step
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 877us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 14: 44.86 [%]
F1-score capturado en la ejecución 14: 42.75 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:32[0m 1s/step
[1m 59/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 865us/step
[1m123/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 822us/step
[1m187/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 809us/step
[1m249/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 809us/step
[1m310/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 813us/step
[1m375/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 806us/step
[1m434/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 813us/step
[1m497/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 812us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m62/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 836us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 851us/step
[1m117/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 869us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 46.4 [%]
Global F1 score (validation) = 44.73 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.6332752e-04 1.1112335e-03 4.9602921e-04 ... 5.0948642e-04
  1.6761060e-03 5.5430259e-04]
 [2.7995368e-03 4.1158060e-03 2.8076433e-03 ... 8.0631382e-04
  3.4144146e-03 9.3643373e-04]
 [1.4115316e-03 1.6063584e-03 1.2508680e-03 ... 5.1063061e-04
  1.9629383e-03 6.0883386e-04]
 ...
 [8.9185914e-06 5.4801517e-06 3.5677137e-06 ... 1.5863245e-03
  8.8453310e-04 4.7515315e-04]
 [7.4946456e-06 4.3307273e-06 2.9047249e-06 ... 1.2129818e-03
  5.8155728e-04 5.9748738e-04]
 [5.0153048e-04 4.8347263e-04 5.8444653e-04 ... 3.0318606e-01
  1.4321875e-03 4.3331925e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 58.07 [%]
Global accuracy score (test) = 45.19 [%]
Global F1 score (train) = 56.36 [%]
Global F1 score (test) = 43.33 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.33      0.26       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.20      0.23       161
       CAMINAR USUAL SPEED       0.27      0.23      0.25       161
            CAMINAR ZIGZAG       0.19      0.13      0.15       161
          DE PIE BARRIENDO       0.43      0.51      0.46       161
   DE PIE DOBLANDO TOALLAS       0.37      0.26      0.31       161
    DE PIE MOVIENDO LIBROS       0.39      0.45      0.42       161
          DE PIE USANDO PC       0.78      0.84      0.81       161
        FASE REPOSO CON K5       0.40      0.88      0.55       161
INCREMENTAL CICLOERGOMETRO       0.99      0.91      0.95       161
           SENTADO LEYENDO       0.35      0.45      0.39       161
         SENTADO USANDO PC       0.67      0.07      0.13       161
      SENTADO VIENDO LA TV       0.40      0.12      0.19       161
   SUBIR Y BAJAR ESCALERAS       0.51      0.67      0.58       161
                    TROTAR       0.88      0.76      0.81       138

                  accuracy                           0.45      2392
                 macro avg       0.47      0.45      0.43      2392
              weighted avg       0.47      0.45      0.43      2392

2025-10-27 12:36:06.990607: 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`.
2025-10-27 12:36:07.003203: 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 00:00:1761564967.017197  710063 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761564967.021396  710063 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761564967.032468  710063 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564967.032491  710063 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564967.032494  710063 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761564967.032496  710063 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:36:07.035878: 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 00:00:1761564969.407180  710063 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761564971.975024  710220 service.cc:152] XLA service 0x7b7458023180 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761564971.975078  710220 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:36:12.031588: 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 00:00:1761564972.338224  710220 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761564976.242965  710220 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:45[0m 6s/step - accuracy: 0.0703 - loss: 3.1939
[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0801 - loss: 3.0913  
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0866 - loss: 3.0380
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0939 - loss: 2.9980
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1003 - loss: 2.9669
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1058 - loss: 2.9378
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1105 - loss: 2.91252025-10-27 12:36:18.201730: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:36:20.843818: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1140 - loss: 2.89372025-10-27 12:36:22.963275: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1141 - loss: 2.8927 - val_accuracy: 0.2292 - val_loss: 2.1134
Epoch 2/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1562 - loss: 2.6380
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1849 - loss: 2.5543 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1868 - loss: 2.5351
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1876 - loss: 2.5171
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1883 - loss: 2.5045
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1890 - loss: 2.4945
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.1894 - loss: 2.4855
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1898 - loss: 2.4806 - val_accuracy: 0.2949 - val_loss: 1.8279
Epoch 3/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.4008
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2049 - loss: 2.3303 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2136 - loss: 2.3036
[1m 64/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2182 - loss: 2.2882
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2210 - loss: 2.2770
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2225 - loss: 2.2696
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.2240 - loss: 2.2626
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2249 - loss: 2.2583 - val_accuracy: 0.3279 - val_loss: 1.6770
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2734 - loss: 2.1263
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2435 - loss: 2.1442 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2487 - loss: 2.1341
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2524 - loss: 2.1253
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2546 - loss: 2.1189
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2559 - loss: 2.1144
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2569 - loss: 2.1102
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2578 - loss: 2.1058 - val_accuracy: 0.3429 - val_loss: 1.6233
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 1.8513
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2858 - loss: 1.9684 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2832 - loss: 1.9797
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2808 - loss: 1.9831
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2802 - loss: 1.9842
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2802 - loss: 1.9836
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2804 - loss: 1.9818
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2807 - loss: 1.9799 - val_accuracy: 0.3757 - val_loss: 1.5376
Epoch 6/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.0707
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3063 - loss: 1.9174 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3053 - loss: 1.9056
[1m 64/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3039 - loss: 1.9031
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3034 - loss: 1.9004
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3029 - loss: 1.8972
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3021 - loss: 1.8951
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3017 - loss: 1.8939 - val_accuracy: 0.4073 - val_loss: 1.4691
Epoch 7/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 1.9935
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3325 - loss: 1.8641 
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[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3330 - loss: 1.8392
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3324 - loss: 1.8320
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3325 - loss: 1.8275
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3327 - loss: 1.8245
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3327 - loss: 1.8216
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Epoch 8/92

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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3287 - loss: 1.7901
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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3335 - loss: 1.7799
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Epoch 9/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3666 - loss: 1.7005
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3653 - loss: 1.6980
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3650 - loss: 1.6969
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3651 - loss: 1.6949
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Epoch 10/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3596 - loss: 1.6609 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3631 - loss: 1.6633
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3660 - loss: 1.6639
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3692 - loss: 1.6614
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3716 - loss: 1.6593
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3732 - loss: 1.6572
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3739 - loss: 1.6557 - val_accuracy: 0.4534 - val_loss: 1.3611
Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3938 - loss: 1.5872 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3897 - loss: 1.5939
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3924 - loss: 1.5970
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3930 - loss: 1.5977
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3936 - loss: 1.5980
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3942 - loss: 1.5977 - val_accuracy: 0.4464 - val_loss: 1.3885
Epoch 12/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4042 - loss: 1.5748 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4016 - loss: 1.5731
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4014 - loss: 1.5716
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4018 - loss: 1.5705
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4023 - loss: 1.5686
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4028 - loss: 1.5672 - val_accuracy: 0.4435 - val_loss: 1.3628
Epoch 13/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4141 - loss: 1.6157
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4213 - loss: 1.5386 
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Epoch 14/92

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Epoch 15/92

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Epoch 16/92

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[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4396 - loss: 1.4347
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Epoch 17/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4676 - loss: 1.3752 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4554 - loss: 1.4073
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Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4716 - loss: 1.3483 
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Epoch 19/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4591 - loss: 1.3845 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4574 - loss: 1.3961
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4561 - loss: 1.3880
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4567 - loss: 1.3844
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4585 - loss: 1.3802 - val_accuracy: 0.4636 - val_loss: 1.3551

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 1s/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 894us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 15: 45.19 [%]
F1-score capturado en la ejecución 15: 43.33 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 61/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 839us/step
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[1m182/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 834us/step
[1m243/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 833us/step
[1m308/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 821us/step
[1m366/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 828us/step
[1m432/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 818us/step
[1m496/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 815us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 870us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 849us/step
[1m126/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 804us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 46.32 [%]
Global F1 score (validation) = 44.34 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.9323928e-04 9.2405878e-04 4.3910687e-04 ... 2.0480808e-04
  7.0401607e-04 2.3957143e-04]
 [2.0746512e-03 2.3873611e-03 1.2197396e-03 ... 4.3995967e-04
  9.2248927e-04 5.0497695e-04]
 [4.9245427e-04 7.0180220e-04 3.1568241e-04 ... 2.6220368e-04
  3.8823709e-04 2.5198882e-04]
 ...
 [1.5824742e-05 1.1267793e-05 1.0112133e-05 ... 1.0636598e-03
  8.0572959e-04 2.4976111e-03]
 [2.3380515e-05 1.5327072e-05 1.6379036e-05 ... 2.4761285e-03
  7.6407392e-04 2.4422433e-03]
 [1.2614307e-03 6.7632197e-04 1.4260905e-03 ... 2.2389616e-01
  3.5860443e-03 9.1057243e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 54.48 [%]
Global accuracy score (test) = 47.07 [%]
Global F1 score (train) = 52.26 [%]
Global F1 score (test) = 44.31 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.11      0.14       161
 CAMINAR CON MÓVIL O LIBRO       0.28      0.20      0.24       161
       CAMINAR USUAL SPEED       0.28      0.50      0.35       161
            CAMINAR ZIGZAG       0.23      0.16      0.19       161
          DE PIE BARRIENDO       0.43      0.44      0.43       161
   DE PIE DOBLANDO TOALLAS       0.31      0.29      0.30       161
    DE PIE MOVIENDO LIBROS       0.34      0.39      0.37       161
          DE PIE USANDO PC       0.77      0.81      0.79       161
        FASE REPOSO CON K5       0.57      0.86      0.68       161
INCREMENTAL CICLOERGOMETRO       0.97      0.93      0.95       161
           SENTADO LEYENDO       0.39      0.79      0.52       161
         SENTADO USANDO PC       0.54      0.04      0.08       161
      SENTADO VIENDO LA TV       0.53      0.13      0.21       161
   SUBIR Y BAJAR ESCALERAS       0.48      0.71      0.57       161
                    TROTAR       0.93      0.75      0.83       138

                  accuracy                           0.47      2392
                 macro avg       0.48      0.47      0.44      2392
              weighted avg       0.48      0.47      0.44      2392

2025-10-27 12:36:46.953727: 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`.
2025-10-27 12:36:46.966358: 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 00:00:1761565006.979979  713292 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565006.984374  713292 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565006.995258  713292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565006.995297  713292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565006.995301  713292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565006.995304  713292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:36:46.998688: 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 00:00:1761565009.411405  713292 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565011.997235  713442 service.cc:152] XLA service 0x7ec14c012cd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565011.997311  713442 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:36:52.056843: 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 00:00:1761565012.361899  713442 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565016.414356  713442 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:07[0m 6s/step - accuracy: 0.0859 - loss: 3.1794
[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0816 - loss: 3.1124  
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[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0988 - loss: 2.9909
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1042 - loss: 2.9578
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1089 - loss: 2.9295
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1127 - loss: 2.90532025-10-27 12:36:58.250234: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:37:00.989349: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1150 - loss: 2.89102025-10-27 12:37:03.236825: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 54ms/step - accuracy: 0.1151 - loss: 2.8900 - val_accuracy: 0.2287 - val_loss: 2.1521
Epoch 2/92

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[1m 77/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1722 - loss: 2.5206
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1749 - loss: 2.5090
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1773 - loss: 2.4990
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1792 - loss: 2.4904
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.1793 - loss: 2.4900 - val_accuracy: 0.2889 - val_loss: 1.8083
Epoch 3/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2065 - loss: 2.3008
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2089 - loss: 2.2895
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2112 - loss: 2.2818
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2132 - loss: 2.2750
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2150 - loss: 2.2686
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2164 - loss: 2.2633 - val_accuracy: 0.3291 - val_loss: 1.6837
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2656 - loss: 2.0206
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2623 - loss: 2.1302 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2583 - loss: 2.1293
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2584 - loss: 2.1281
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2594 - loss: 2.1260
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2603 - loss: 2.1216
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2614 - loss: 2.1157
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2621 - loss: 2.1112 - val_accuracy: 0.3502 - val_loss: 1.6118
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.0008
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2765 - loss: 2.0144 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2830 - loss: 1.9986
[1m 68/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2853 - loss: 1.9923
[1m 91/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2860 - loss: 1.9891
[1m113/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2866 - loss: 1.9865
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2877 - loss: 1.9826
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2879 - loss: 1.9820 - val_accuracy: 0.3802 - val_loss: 1.5102
Epoch 6/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 1.8880
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2908 - loss: 1.9341 
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[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3011 - loss: 1.9161
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3043 - loss: 1.9083
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3062 - loss: 1.9020
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Epoch 7/92

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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3334 - loss: 1.8069
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3336 - loss: 1.8052
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3337 - loss: 1.8041
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Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3292 - loss: 1.7880 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3325 - loss: 1.7845
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3385 - loss: 1.7707
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3398 - loss: 1.7660
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3414 - loss: 1.7616
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3425 - loss: 1.7585 - val_accuracy: 0.4267 - val_loss: 1.4102
Epoch 9/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3612 - loss: 1.7261 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3590 - loss: 1.7159
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3568 - loss: 1.7106
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3570 - loss: 1.7076
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3572 - loss: 1.7052
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3574 - loss: 1.7039 - val_accuracy: 0.4441 - val_loss: 1.3786
Epoch 10/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3804 - loss: 1.6575 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3771 - loss: 1.6548
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3774 - loss: 1.6522
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3782 - loss: 1.6505
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3785 - loss: 1.6496
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3787 - loss: 1.6489
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3788 - loss: 1.6479 - val_accuracy: 0.4358 - val_loss: 1.3821
Epoch 11/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3906 - loss: 1.5660
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4007 - loss: 1.5898 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4009 - loss: 1.5921
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4009 - loss: 1.5916
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4010 - loss: 1.5918
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4006 - loss: 1.5912
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4003 - loss: 1.5908
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4001 - loss: 1.5900 - val_accuracy: 0.4538 - val_loss: 1.3651
Epoch 12/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4922 - loss: 1.3975
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4190 - loss: 1.5235 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4188 - loss: 1.5288
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4172 - loss: 1.5380
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4159 - loss: 1.5417
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Epoch 13/92

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[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4094 - loss: 1.5328
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Epoch 14/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4216 - loss: 1.5128
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Epoch 15/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4157 - loss: 1.4695 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4183 - loss: 1.4669
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4202 - loss: 1.4669
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4215 - loss: 1.4666
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4228 - loss: 1.4659
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4245 - loss: 1.4644
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4247 - loss: 1.4643 - val_accuracy: 0.4621 - val_loss: 1.3533
Epoch 16/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4375 - loss: 1.4098 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4386 - loss: 1.4198
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4414 - loss: 1.4231
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4420 - loss: 1.4243
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4429 - loss: 1.4238
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4433 - loss: 1.4237 - val_accuracy: 0.4621 - val_loss: 1.3680
Epoch 17/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4337 - loss: 1.4194 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4377 - loss: 1.4194
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4409 - loss: 1.4190
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Epoch 18/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4638 - loss: 1.3617 
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Epoch 19/92

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 1s/step
[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 873us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 47.07 [%]
F1-score capturado en la ejecución 16: 44.31 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 59/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 863us/step
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[1m186/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 814us/step
[1m246/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 819us/step
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[1m375/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 808us/step
[1m434/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 814us/step
[1m493/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 819us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 55/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 941us/step
[1m114/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 898us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 47.25 [%]
Global F1 score (validation) = 45.81 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[3.0183987e-04 4.7915283e-04 2.2702981e-04 ... 1.3337279e-04
  4.1238329e-04 9.5727890e-05]
 [1.4195363e-03 2.9042312e-03 1.7596877e-03 ... 9.3136402e-04
  2.3310594e-03 3.1657075e-04]
 [5.7631213e-04 9.6781831e-04 6.6191220e-04 ... 6.0636678e-04
  1.0561582e-03 1.4025295e-04]
 ...
 [1.0786985e-05 4.0456671e-06 3.4391564e-06 ... 6.1205751e-04
  4.9399701e-04 1.1095488e-03]
 [1.5776492e-05 5.1046586e-06 5.1715092e-06 ... 1.3486630e-03
  4.9120898e-04 1.4757641e-03]
 [7.8169466e-04 3.4214536e-04 5.1976385e-04 ... 3.2122308e-01
  1.6690090e-03 2.3431159e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 55.45 [%]
Global accuracy score (test) = 45.32 [%]
Global F1 score (train) = 53.68 [%]
Global F1 score (test) = 43.46 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.12      0.16       161
 CAMINAR CON MÓVIL O LIBRO       0.30      0.35      0.32       161
       CAMINAR USUAL SPEED       0.27      0.40      0.32       161
            CAMINAR ZIGZAG       0.20      0.09      0.12       161
          DE PIE BARRIENDO       0.42      0.43      0.43       161
   DE PIE DOBLANDO TOALLAS       0.28      0.16      0.20       161
    DE PIE MOVIENDO LIBROS       0.38      0.60      0.46       161
          DE PIE USANDO PC       0.80      0.80      0.80       161
        FASE REPOSO CON K5       0.52      0.87      0.65       161
INCREMENTAL CICLOERGOMETRO       0.97      0.93      0.95       161
           SENTADO LEYENDO       0.39      0.27      0.32       161
         SENTADO USANDO PC       0.68      0.09      0.16       161
      SENTADO VIENDO LA TV       0.16      0.22      0.18       161
   SUBIR Y BAJAR ESCALERAS       0.51      0.76      0.61       161
                    TROTAR       0.91      0.76      0.83       138

                  accuracy                           0.45      2392
                 macro avg       0.47      0.46      0.43      2392
              weighted avg       0.46      0.45      0.43      2392

2025-10-27 12:37:27.032001: 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`.
2025-10-27 12:37:27.044672: 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 00:00:1761565047.058257  716505 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565047.062577  716505 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565047.073628  716505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565047.073651  716505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565047.073662  716505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565047.073665  716505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:37:27.077045: 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 00:00:1761565049.476177  716505 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565052.117574  716648 service.cc:152] XLA service 0x735664012800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565052.117648  716648 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:37:32.172809: 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 00:00:1761565052.474255  716648 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565056.447995  716648 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:02[0m 6s/step - accuracy: 0.0391 - loss: 3.3597
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1087 - loss: 2.9886
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1126 - loss: 2.9606
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1157 - loss: 2.93742025-10-27 12:37:38.438651: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:37:41.079322: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1179 - loss: 2.92132025-10-27 12:37:43.143218: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 54ms/step - accuracy: 0.1181 - loss: 2.9203 - val_accuracy: 0.1907 - val_loss: 2.1550
Epoch 2/92

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[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1684 - loss: 2.5347
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1710 - loss: 2.5225
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1731 - loss: 2.5127 - val_accuracy: 0.2820 - val_loss: 1.8311
Epoch 3/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2097 - loss: 2.2949
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2117 - loss: 2.2894
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2136 - loss: 2.2839
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2151 - loss: 2.2793 - val_accuracy: 0.3338 - val_loss: 1.7134
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2734 - loss: 2.1092
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2546 - loss: 2.1306
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2549 - loss: 2.1291
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2551 - loss: 2.1269
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2554 - loss: 2.1247 - val_accuracy: 0.3577 - val_loss: 1.6135
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3438 - loss: 1.9018
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[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2834 - loss: 2.0059
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2838 - loss: 2.0023
[1m134/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.2844 - loss: 1.9987
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Epoch 6/92

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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3066 - loss: 1.8958
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3054 - loss: 1.8973
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.3047 - loss: 1.8984
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Epoch 7/92

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[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3146 - loss: 1.8607
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3161 - loss: 1.8550
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3172 - loss: 1.8509 - val_accuracy: 0.4136 - val_loss: 1.4821
Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3305 - loss: 1.8188 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3328 - loss: 1.8011
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3333 - loss: 1.7907
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3333 - loss: 1.7868
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3338 - loss: 1.7828
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3347 - loss: 1.7801
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Epoch 9/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3649 - loss: 1.6487 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3587 - loss: 1.6736
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3573 - loss: 1.6911
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3578 - loss: 1.6935
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3584 - loss: 1.6947
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3590 - loss: 1.6957
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3590 - loss: 1.6957 - val_accuracy: 0.4180 - val_loss: 1.4251
Epoch 10/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3590 - loss: 1.6649 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3720 - loss: 1.6642
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3730 - loss: 1.6659
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3738 - loss: 1.6660
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Epoch 11/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3844 - loss: 1.6767 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3812 - loss: 1.6571
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3812 - loss: 1.6522
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Epoch 12/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4117 - loss: 1.5836
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Epoch 13/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4158 - loss: 1.5106
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[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4150 - loss: 1.5160
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4148 - loss: 1.5183
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4148 - loss: 1.5186 - val_accuracy: 0.4328 - val_loss: 1.4011
Epoch 14/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4435 - loss: 1.5072 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4387 - loss: 1.4999
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4354 - loss: 1.4936
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[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4345 - loss: 1.4919
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4342 - loss: 1.4921 - val_accuracy: 0.4543 - val_loss: 1.3593
Epoch 15/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4160 - loss: 1.4987 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4208 - loss: 1.4990
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4274 - loss: 1.4930
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4296 - loss: 1.4890
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4315 - loss: 1.4858
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4327 - loss: 1.4834 - val_accuracy: 0.4587 - val_loss: 1.3512
Epoch 16/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4062 - loss: 1.4167
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4403 - loss: 1.4329 
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4423 - loss: 1.4408
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4435 - loss: 1.4391
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4442 - loss: 1.4384
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Epoch 17/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4355 - loss: 1.4793 
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[1m 57/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4410 - loss: 1.4497
[1m 77/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4423 - loss: 1.4445
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4433 - loss: 1.4407
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4443 - loss: 1.4376
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4452 - loss: 1.4348
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4454 - loss: 1.4345 - val_accuracy: 0.4617 - val_loss: 1.3698
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4609 - loss: 1.2844
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[1m 96/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4572 - loss: 1.3764
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4572 - loss: 1.3793
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4571 - loss: 1.3818 - val_accuracy: 0.4494 - val_loss: 1.3715
Epoch 19/92

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[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4597 - loss: 1.3506 
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[1m 76/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4613 - loss: 1.3705
[1m 95/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4619 - loss: 1.3718
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4630 - loss: 1.3715
[1m132/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4637 - loss: 1.3706
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 3ms/step - accuracy: 0.4639 - loss: 1.3703 - val_accuracy: 0.4664 - val_loss: 1.3710
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.5156 - loss: 1.2765
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4746 - loss: 1.3361 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4709 - loss: 1.3500
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4714 - loss: 1.3509
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4720 - loss: 1.3508
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4725 - loss: 1.3511 - val_accuracy: 0.4753 - val_loss: 1.3790

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16[0m 1s/step
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 915us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 17: 45.32 [%]
F1-score capturado en la ejecución 17: 43.46 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 59/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 868us/step
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[1m184/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 825us/step
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[1m306/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 826us/step
[1m372/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 815us/step
[1m438/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 808us/step
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 58/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 879us/step
[1m120/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 843us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 47.61 [%]
Global F1 score (validation) = 46.47 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.3392708e-04 1.1296888e-03 3.7467404e-04 ... 2.1893969e-04
  3.9851223e-04 2.9941168e-04]
 [8.5565896e-04 2.5330754e-03 9.9126494e-04 ... 6.7057891e-04
  1.3145346e-03 5.4799061e-04]
 [2.9436394e-04 1.0294889e-03 3.6622572e-04 ... 4.4994475e-04
  8.6112513e-04 3.0768529e-04]
 ...
 [1.4029985e-05 9.0799749e-06 1.2659414e-05 ... 1.0890096e-03
  9.7370404e-04 1.2357578e-03]
 [9.7780685e-06 7.0997680e-06 8.7287353e-06 ... 1.5342077e-03
  7.5101660e-04 1.0476706e-03]
 [5.9296185e-04 3.6340623e-04 6.8968011e-04 ... 1.9040513e-01
  2.1245941e-03 3.5962746e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.99 [%]
Global accuracy score (test) = 44.02 [%]
Global F1 score (train) = 55.73 [%]
Global F1 score (test) = 42.81 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.17      0.19       161
 CAMINAR CON MÓVIL O LIBRO       0.21      0.29      0.24       161
       CAMINAR USUAL SPEED       0.21      0.19      0.20       161
            CAMINAR ZIGZAG       0.18      0.14      0.16       161
          DE PIE BARRIENDO       0.39      0.39      0.39       161
   DE PIE DOBLANDO TOALLAS       0.33      0.40      0.36       161
    DE PIE MOVIENDO LIBROS       0.38      0.33      0.35       161
          DE PIE USANDO PC       0.76      0.81      0.79       161
        FASE REPOSO CON K5       0.53      0.87      0.66       161
INCREMENTAL CICLOERGOMETRO       0.94      0.93      0.94       161
           SENTADO LEYENDO       0.33      0.42      0.37       161
         SENTADO USANDO PC       0.64      0.11      0.19       161
      SENTADO VIENDO LA TV       0.18      0.14      0.16       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.70      0.59       161
                    TROTAR       0.93      0.76      0.84       138

                  accuracy                           0.44      2392
                 macro avg       0.45      0.44      0.43      2392
              weighted avg       0.45      0.44      0.42      2392

2025-10-27 12:38:07.884794: 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`.
2025-10-27 12:38:07.897668: 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 00:00:1761565087.911422  719814 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565087.915586  719814 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565087.926868  719814 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565087.926890  719814 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565087.926893  719814 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565087.926896  719814 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:38:07.930214: 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 00:00:1761565090.347096  719814 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12389 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565092.928661  719965 service.cc:152] XLA service 0x76ee54013ef0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565092.928702  719965 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:38:12.987331: 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 00:00:1761565093.295965  719965 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565097.197031  719965 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:48[0m 6s/step - accuracy: 0.0781 - loss: 3.2382
[1m 17/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0749 - loss: 3.1208  
[1m 37/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0853 - loss: 3.0653
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[1m 74/137[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1006 - loss: 2.9962
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[1m113/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1098 - loss: 2.9444
[1m132/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.1133 - loss: 2.92342025-10-27 12:38:18.952330: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:38:21.804819: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1141 - loss: 2.91822025-10-27 12:38:23.937505: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1143 - loss: 2.9172 - val_accuracy: 0.2281 - val_loss: 2.1491
Epoch 2/92

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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1767 - loss: 2.5447
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Epoch 3/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2193 - loss: 2.2873
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Epoch 4/92

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[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2531 - loss: 2.1556 
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[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2531 - loss: 2.1343
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.2537 - loss: 2.1299
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2537 - loss: 2.1295 - val_accuracy: 0.3439 - val_loss: 1.6403
Epoch 5/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2639 - loss: 2.0401 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2695 - loss: 2.0305
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2751 - loss: 2.0177
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2770 - loss: 2.0133
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2781 - loss: 2.0101
[1m134/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.2790 - loss: 2.0071
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.2791 - loss: 2.0065 - val_accuracy: 0.3648 - val_loss: 1.5736
Epoch 6/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3003 - loss: 1.9268 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3028 - loss: 1.9220
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3021 - loss: 1.9195
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3022 - loss: 1.9177
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3023 - loss: 1.9148
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3027 - loss: 1.9123 - val_accuracy: 0.3769 - val_loss: 1.5174
Epoch 7/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3345 - loss: 1.8201 
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3335 - loss: 1.8106
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3328 - loss: 1.8115
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3323 - loss: 1.8125
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Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3461 - loss: 1.7982 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3391 - loss: 1.7861
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3396 - loss: 1.7812
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Epoch 9/92

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[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3515 - loss: 1.7312
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Epoch 10/92

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[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3664 - loss: 1.6832
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3671 - loss: 1.6800 - val_accuracy: 0.4253 - val_loss: 1.3857
Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3720 - loss: 1.6122 
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3734 - loss: 1.6250
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3749 - loss: 1.6249
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3761 - loss: 1.6245
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3768 - loss: 1.6238 - val_accuracy: 0.4318 - val_loss: 1.3815
Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4241 - loss: 1.5336 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4201 - loss: 1.5438
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4138 - loss: 1.5576
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4120 - loss: 1.5597
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4108 - loss: 1.5609
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4099 - loss: 1.5619 - val_accuracy: 0.4425 - val_loss: 1.3657
Epoch 13/92

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[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4047 - loss: 1.5664 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4079 - loss: 1.5632
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4104 - loss: 1.5598
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4110 - loss: 1.5589
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4115 - loss: 1.5578
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4120 - loss: 1.5558
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Epoch 14/92

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Epoch 15/92

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Epoch 16/92

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Epoch 17/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4358 - loss: 1.4452 
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Epoch 18/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4390 - loss: 1.4267 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4478 - loss: 1.4120
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4487 - loss: 1.4118
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4493 - loss: 1.4116
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4494 - loss: 1.4113
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.4494 - loss: 1.4112 - val_accuracy: 0.4587 - val_loss: 1.3753
Epoch 19/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4829 - loss: 1.3250 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4760 - loss: 1.3475
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Epoch 20/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4775 - loss: 1.3302 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4751 - loss: 1.3481
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4729 - loss: 1.3602
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4731 - loss: 1.3610
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4730 - loss: 1.3612
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4728 - loss: 1.3613 - val_accuracy: 0.4573 - val_loss: 1.4031

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 1s/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 909us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 44.02 [%]
F1-score capturado en la ejecución 18: 42.81 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 54/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 944us/step
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[1m178/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 849us/step
[1m239/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 843us/step
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[1m360/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 839us/step
[1m423/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 833us/step
[1m483/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 833us/step
[1m546/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 830us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 811us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 67/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 765us/step
[1m132/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 770us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 45.73 [%]
Global F1 score (validation) = 44.29 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[6.02183747e-04 1.12502940e-03 3.53328447e-04 ... 2.93481164e-04
  7.55560119e-04 2.84923881e-04]
 [1.31984486e-03 2.43189000e-03 8.23523151e-04 ... 6.67374756e-04
  1.08212186e-03 6.69149042e-04]
 [1.14738930e-03 1.83824054e-03 6.99078839e-04 ... 4.45571379e-04
  7.13650195e-04 3.70366237e-04]
 ...
 [1.36637855e-05 1.46858129e-05 1.03385873e-05 ... 2.21458194e-03
  1.06663269e-03 2.02435907e-03]
 [9.11639654e-06 1.14877867e-05 7.11082794e-06 ... 1.25161617e-03
  9.79247969e-04 2.50166096e-03]
 [3.59429832e-04 4.36742645e-04 5.69966505e-04 ... 2.48021647e-01
  1.49071764e-03 7.49583915e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 57.0 [%]
Global accuracy score (test) = 45.19 [%]
Global F1 score (train) = 55.48 [%]
Global F1 score (test) = 42.72 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.17      0.17       161
 CAMINAR CON MÓVIL O LIBRO       0.19      0.28      0.23       161
       CAMINAR USUAL SPEED       0.23      0.20      0.22       161
            CAMINAR ZIGZAG       0.08      0.04      0.06       161
          DE PIE BARRIENDO       0.41      0.29      0.34       161
   DE PIE DOBLANDO TOALLAS       0.32      0.32      0.32       161
    DE PIE MOVIENDO LIBROS       0.38      0.52      0.44       161
          DE PIE USANDO PC       0.76      0.83      0.79       161
        FASE REPOSO CON K5       0.49      0.88      0.63       161
INCREMENTAL CICLOERGOMETRO       0.96      0.94      0.95       161
           SENTADO LEYENDO       0.45      0.77      0.56       161
         SENTADO USANDO PC       0.48      0.06      0.11       161
      SENTADO VIENDO LA TV       0.51      0.12      0.19       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.68      0.60       161
                    TROTAR       0.92      0.72      0.81       138

                  accuracy                           0.45      2392
                 macro avg       0.46      0.45      0.43      2392
              weighted avg       0.45      0.45      0.42      2392

2025-10-27 12:38:48.659825: 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`.
2025-10-27 12:38:48.672544: 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 00:00:1761565128.686745  723130 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565128.691212  723130 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565128.702457  723130 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565128.702484  723130 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565128.702487  723130 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565128.702490  723130 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:38:48.705763: 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 00:00:1761565131.142026  723130 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12391 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565133.746600  723282 service.cc:152] XLA service 0x75dfa0003730 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565133.746639  723282 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:38:53.801015: 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 00:00:1761565134.130272  723282 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565138.118172  723282 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:05[0m 6s/step - accuracy: 0.0625 - loss: 3.0242
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0737 - loss: 3.0743  
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[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1000 - loss: 2.9799
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1065 - loss: 2.9514
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1117 - loss: 2.9272
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1155 - loss: 2.90672025-10-27 12:38:59.925826: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:39:02.696560: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1183 - loss: 2.89182025-10-27 12:39:04.745614: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 53ms/step - accuracy: 0.1184 - loss: 2.8909 - val_accuracy: 0.2249 - val_loss: 2.1437
Epoch 2/92

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[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1657 - loss: 2.5247
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1689 - loss: 2.5122
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.1716 - loss: 2.5013 - val_accuracy: 0.2731 - val_loss: 1.8631
Epoch 3/92

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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2266 - loss: 2.2710
[1m 96/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2273 - loss: 2.2667
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2283 - loss: 2.2611
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2294 - loss: 2.2552 - val_accuracy: 0.3245 - val_loss: 1.7102
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.1500
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2378 - loss: 2.1651 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2425 - loss: 2.1488
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2457 - loss: 2.1364
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2481 - loss: 2.1280
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2500 - loss: 2.1211
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2512 - loss: 2.1157
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2523 - loss: 2.1112 - val_accuracy: 0.3522 - val_loss: 1.6139
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 2.0218
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2924 - loss: 2.0012 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2877 - loss: 2.0043
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[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2876 - loss: 1.9918
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2881 - loss: 1.9869
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2887 - loss: 1.9827 - val_accuracy: 0.3755 - val_loss: 1.5300
Epoch 6/92

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[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3054 - loss: 1.8985
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3044 - loss: 1.8959
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3049 - loss: 1.8927
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3051 - loss: 1.8900
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3055 - loss: 1.8877 - val_accuracy: 0.3885 - val_loss: 1.4886
Epoch 7/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3215 - loss: 1.8410
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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3235 - loss: 1.8324
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Epoch 8/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3331 - loss: 1.7622
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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3354 - loss: 1.7582
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3364 - loss: 1.7567 - val_accuracy: 0.4194 - val_loss: 1.4316
Epoch 9/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3455 - loss: 1.7229 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3488 - loss: 1.7229
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3538 - loss: 1.7148
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3548 - loss: 1.7116
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3556 - loss: 1.7089
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3565 - loss: 1.7061 - val_accuracy: 0.4352 - val_loss: 1.3892
Epoch 10/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4142 - loss: 1.6154 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4010 - loss: 1.6249
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3965 - loss: 1.6280
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3938 - loss: 1.6304
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3915 - loss: 1.6328
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3902 - loss: 1.6342
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3894 - loss: 1.6351 - val_accuracy: 0.4372 - val_loss: 1.3815
Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3922 - loss: 1.5669 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3961 - loss: 1.5808
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3942 - loss: 1.5934
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3940 - loss: 1.5956
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3944 - loss: 1.5965
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Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4162 - loss: 1.5209 
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4088 - loss: 1.5526
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4082 - loss: 1.5544
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Epoch 13/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4118 - loss: 1.5242
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4130 - loss: 1.5251
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4137 - loss: 1.5256
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Epoch 14/92

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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4351 - loss: 1.4979
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4350 - loss: 1.4952 - val_accuracy: 0.4571 - val_loss: 1.3569
Epoch 15/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4461 - loss: 1.4266 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4441 - loss: 1.4350
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4410 - loss: 1.4476
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4392 - loss: 1.4518 - val_accuracy: 0.4528 - val_loss: 1.3727
Epoch 16/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4439 - loss: 1.4239 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4420 - loss: 1.4397
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4421 - loss: 1.4417
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4423 - loss: 1.4422
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4428 - loss: 1.4414
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4432 - loss: 1.4408 - val_accuracy: 0.4565 - val_loss: 1.3540
Epoch 17/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4510 - loss: 1.4046 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4504 - loss: 1.4064
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[1m 77/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4479 - loss: 1.4105
[1m 96/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4474 - loss: 1.4126
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4472 - loss: 1.4142
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4473 - loss: 1.4149
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Epoch 18/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4503 - loss: 1.3977 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4530 - loss: 1.3979
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4545 - loss: 1.3957
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4550 - loss: 1.3941
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4555 - loss: 1.3932
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4562 - loss: 1.3920
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4568 - loss: 1.3911 - val_accuracy: 0.4682 - val_loss: 1.3554
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5078 - loss: 1.3163
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4847 - loss: 1.3468 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4771 - loss: 1.3562
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4752 - loss: 1.3565
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4735 - loss: 1.3557
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4720 - loss: 1.3562
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4709 - loss: 1.3570
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4702 - loss: 1.3578 - val_accuracy: 0.4688 - val_loss: 1.3705
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5000 - loss: 1.2788
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4796 - loss: 1.3215 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4794 - loss: 1.3227
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4773 - loss: 1.3298
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4767 - loss: 1.3328
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4761 - loss: 1.3346
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4761 - loss: 1.3355
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4762 - loss: 1.3357 - val_accuracy: 0.4636 - val_loss: 1.3763
Epoch 21/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5000 - loss: 1.3928
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4650 - loss: 1.3646 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4704 - loss: 1.3479
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4742 - loss: 1.3412
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4776 - loss: 1.3364
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4795 - loss: 1.3324
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4805 - loss: 1.3305
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4811 - loss: 1.3297 - val_accuracy: 0.4725 - val_loss: 1.3710
Epoch 22/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5000 - loss: 1.3302
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.5075 - loss: 1.3010 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.5036 - loss: 1.2995
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4993 - loss: 1.3035
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4975 - loss: 1.3054
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4966 - loss: 1.3061
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4963 - loss: 1.3065
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4960 - loss: 1.3068 - val_accuracy: 0.4763 - val_loss: 1.3809

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 1s/step
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 956us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 19: 45.19 [%]
F1-score capturado en la ejecución 19: 42.72 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:39[0m 1s/step
[1m 51/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step  
[1m118/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 862us/step
[1m184/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 828us/step
[1m249/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 813us/step
[1m313/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 808us/step
[1m373/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 813us/step
[1m432/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 819us/step
[1m494/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 819us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 878us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 858us/step
[1m126/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 806us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 47.63 [%]
Global F1 score (validation) = 46.84 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.4949528e-03 2.6686986e-03 1.0556579e-03 ... 7.1739702e-04
  1.7086471e-03 8.2979974e-04]
 [1.5773727e-03 3.9863810e-03 1.1377052e-03 ... 1.2443631e-03
  3.0194407e-03 7.8263757e-04]
 [1.0848863e-03 2.4951561e-03 8.1816333e-04 ... 7.8394759e-04
  1.4702496e-03 4.4017623e-04]
 ...
 [8.9530795e-06 3.9995462e-06 3.6686956e-06 ... 1.4504037e-03
  1.1756407e-03 1.0414652e-03]
 [8.5850434e-06 3.4371674e-06 3.7353816e-06 ... 1.7327626e-03
  6.9897698e-04 1.2423182e-03]
 [4.4179254e-04 2.3987773e-04 5.0452008e-04 ... 2.9935330e-01
  7.6381874e-04 1.6292585e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 58.91 [%]
Global accuracy score (test) = 46.4 [%]
Global F1 score (train) = 58.02 [%]
Global F1 score (test) = 45.4 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.14      0.18       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.30      0.27       161
       CAMINAR USUAL SPEED       0.29      0.38      0.33       161
            CAMINAR ZIGZAG       0.20      0.17      0.18       161
          DE PIE BARRIENDO       0.43      0.37      0.40       161
   DE PIE DOBLANDO TOALLAS       0.37      0.41      0.39       161
    DE PIE MOVIENDO LIBROS       0.40      0.41      0.40       161
          DE PIE USANDO PC       0.78      0.81      0.80       161
        FASE REPOSO CON K5       0.58      0.87      0.69       161
INCREMENTAL CICLOERGOMETRO       0.97      0.93      0.95       161
           SENTADO LEYENDO       0.29      0.37      0.33       161
         SENTADO USANDO PC       0.27      0.10      0.15       161
      SENTADO VIENDO LA TV       0.33      0.25      0.29       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.76      0.62       161
                    TROTAR       0.96      0.73      0.83       138

                  accuracy                           0.46      2392
                 macro avg       0.46      0.47      0.45      2392
              weighted avg       0.46      0.46      0.45      2392

2025-10-27 12:39:30.118440: 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`.
2025-10-27 12:39:30.131120: 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 00:00:1761565170.144855  726629 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565170.149380  726629 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565170.160583  726629 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565170.160610  726629 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565170.160615  726629 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565170.160617  726629 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:39:30.164023: 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 00:00:1761565172.534387  726629 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12391 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565175.094616  726788 service.cc:152] XLA service 0x7514f4013350 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565175.094656  726788 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:39:35.147728: 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 00:00:1761565175.443518  726788 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565179.315315  726788 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:37[0m 6s/step - accuracy: 0.0703 - loss: 3.1492
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0704 - loss: 3.1040  
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0793 - loss: 3.0521
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0884 - loss: 3.0094
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0962 - loss: 2.9729
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1026 - loss: 2.9438
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.1071 - loss: 2.92202025-10-27 12:39:41.323433: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:39:43.979532: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 35ms/step - accuracy: 0.1093 - loss: 2.91042025-10-27 12:39:46.066058: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 55ms/step - accuracy: 0.1095 - loss: 2.9094 - val_accuracy: 0.2219 - val_loss: 2.1615
Epoch 2/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1557 - loss: 2.6226 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1651 - loss: 2.5697
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Epoch 3/92

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Epoch 4/92

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[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2618 - loss: 2.1142
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.2621 - loss: 2.1103
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Epoch 5/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2730 - loss: 2.0032 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2796 - loss: 1.9937
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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2811 - loss: 1.9901
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Epoch 6/92

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[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3141 - loss: 1.8744 
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3073 - loss: 1.8863
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3069 - loss: 1.8861 - val_accuracy: 0.3860 - val_loss: 1.5089
Epoch 7/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3318 - loss: 1.8131 
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 1.8292
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Epoch 8/92

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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3366 - loss: 1.7903
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3384 - loss: 1.7831
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Epoch 9/92

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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3641 - loss: 1.7142
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Epoch 10/92

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[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3882 - loss: 1.6418
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Epoch 11/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4043 - loss: 1.6054 
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[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3959 - loss: 1.6129
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3953 - loss: 1.6119
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.3950 - loss: 1.6101
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3949 - loss: 1.6088 - val_accuracy: 0.4350 - val_loss: 1.3779
Epoch 12/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4148 - loss: 1.5513 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4181 - loss: 1.5457
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4168 - loss: 1.5491
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4156 - loss: 1.5520
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4147 - loss: 1.5535
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4142 - loss: 1.5545
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4141 - loss: 1.5547
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4141 - loss: 1.5547 - val_accuracy: 0.4429 - val_loss: 1.3783
Epoch 13/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4143 - loss: 1.5089 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4130 - loss: 1.5215
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4126 - loss: 1.5262
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4134 - loss: 1.5255
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4142 - loss: 1.5252
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Epoch 14/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4305 - loss: 1.4964 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4298 - loss: 1.4951
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4301 - loss: 1.4940
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4301 - loss: 1.4927
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Epoch 15/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4403 - loss: 1.4673
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4401 - loss: 1.4679
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Epoch 16/92

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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4340 - loss: 1.4403
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Epoch 17/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4697 - loss: 1.3617 
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4628 - loss: 1.3892
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4607 - loss: 1.3932
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4593 - loss: 1.3957
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4587 - loss: 1.3970 - val_accuracy: 0.4615 - val_loss: 1.3602
Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4545 - loss: 1.3796 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4579 - loss: 1.3804
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4606 - loss: 1.3801
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4614 - loss: 1.3805
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4614 - loss: 1.3807
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4614 - loss: 1.3812
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4610 - loss: 1.3822 - val_accuracy: 0.4561 - val_loss: 1.3662
Epoch 19/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4632 - loss: 1.3533 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4642 - loss: 1.3638
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4619 - loss: 1.3726
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4622 - loss: 1.3727
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4626 - loss: 1.3729
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Epoch 20/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4871 - loss: 1.3636 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4817 - loss: 1.3683
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4782 - loss: 1.3678
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4786 - loss: 1.3650
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4789 - loss: 1.3630
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4791 - loss: 1.3614 - val_accuracy: 0.4593 - val_loss: 1.3749

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16[0m 1s/step
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 954us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 46.4 [%]
F1-score capturado en la ejecución 20: 45.4 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:02[0m 1s/step
[1m 58/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 880us/step
[1m124/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 819us/step
[1m187/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 814us/step
[1m252/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 803us/step
[1m311/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 812us/step
[1m378/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 801us/step
[1m445/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 794us/step
[1m514/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 786us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 843us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 56/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 911us/step
[1m114/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 900us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 45.93 [%]
Global F1 score (validation) = 44.35 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.08924199e-04 1.20546820e-03 5.27846394e-04 ... 2.78836989e-04
  5.98435523e-04 2.38194945e-04]
 [2.67326157e-03 5.46580600e-03 3.00771766e-03 ... 1.05250964e-03
  1.21717970e-03 5.75974176e-04]
 [9.79414559e-04 1.72904658e-03 8.54668848e-04 ... 6.40946964e-04
  5.53271908e-04 2.36679654e-04]
 ...
 [8.54466907e-06 9.83298742e-06 1.04389292e-05 ... 1.49657321e-03
  4.69165359e-04 1.25942996e-03]
 [1.00894767e-05 1.19438118e-05 1.23085965e-05 ... 1.78396597e-03
  4.81505704e-04 1.61715085e-03]
 [3.13922879e-04 2.88451090e-04 6.55209296e-04 ... 2.69030511e-01
  6.29185699e-04 3.44696431e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 57.4 [%]
Global accuracy score (test) = 45.9 [%]
Global F1 score (train) = 56.38 [%]
Global F1 score (test) = 44.81 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.20      0.19       161
 CAMINAR CON MÓVIL O LIBRO       0.21      0.30      0.25       161
       CAMINAR USUAL SPEED       0.22      0.21      0.22       161
            CAMINAR ZIGZAG       0.27      0.09      0.13       161
          DE PIE BARRIENDO       0.41      0.42      0.42       161
   DE PIE DOBLANDO TOALLAS       0.29      0.21      0.25       161
    DE PIE MOVIENDO LIBROS       0.35      0.45      0.39       161
          DE PIE USANDO PC       0.72      0.83      0.77       161
        FASE REPOSO CON K5       0.53      0.87      0.66       161
INCREMENTAL CICLOERGOMETRO       0.97      0.91      0.94       161
           SENTADO LEYENDO       0.46      0.40      0.43       161
         SENTADO USANDO PC       0.45      0.49      0.47       161
      SENTADO VIENDO LA TV       0.40      0.12      0.18       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.66      0.59       161
                    TROTAR       0.95      0.77      0.85       138

                  accuracy                           0.46      2392
                 macro avg       0.46      0.46      0.45      2392
              weighted avg       0.46      0.46      0.44      2392

2025-10-27 12:40:10.646494: 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`.
2025-10-27 12:40:10.659276: 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 00:00:1761565210.673236  729959 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565210.677714  729959 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565210.688974  729959 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565210.689001  729959 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565210.689004  729959 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565210.689006  729959 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:40:10.692231: 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 00:00:1761565213.103064  729959 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12391 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565215.684337  730116 service.cc:152] XLA service 0x70a984012810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565215.684417  730116 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:40:15.743900: 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 00:00:1761565216.052115  730116 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565219.932957  730116 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:45[0m 6s/step - accuracy: 0.0312 - loss: 3.2677
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0752 - loss: 3.0840  
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0888 - loss: 3.0124
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0967 - loss: 2.9764
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1027 - loss: 2.9484
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1077 - loss: 2.9239
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1115 - loss: 2.90312025-10-27 12:40:21.654395: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:40:24.605129: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 35ms/step - accuracy: 0.1140 - loss: 2.88832025-10-27 12:40:26.713036: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1142 - loss: 2.8874 - val_accuracy: 0.2348 - val_loss: 2.1095
Epoch 2/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1648 - loss: 2.5561 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1682 - loss: 2.5466
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1717 - loss: 2.5350
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1747 - loss: 2.5236
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1773 - loss: 2.5129
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1792 - loss: 2.5038
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.1807 - loss: 2.4957
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1809 - loss: 2.4947 - val_accuracy: 0.2955 - val_loss: 1.9292
Epoch 3/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1906 - loss: 2.3698 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1997 - loss: 2.3473
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2063 - loss: 2.3266
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2105 - loss: 2.3119
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2137 - loss: 2.3003
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2159 - loss: 2.2915
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2173 - loss: 2.2861 - val_accuracy: 0.3296 - val_loss: 1.6965
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2031 - loss: 2.2139
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2443 - loss: 2.1241 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2475 - loss: 2.1177
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2494 - loss: 2.1157
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2510 - loss: 2.1138
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2526 - loss: 2.1111
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2542 - loss: 2.1069
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2553 - loss: 2.1036 - val_accuracy: 0.3573 - val_loss: 1.6007
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3203 - loss: 1.8785
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2882 - loss: 1.9967 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2836 - loss: 2.0007
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2834 - loss: 2.0005
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2832 - loss: 1.9993
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2832 - loss: 1.9973
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2835 - loss: 1.9946
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2838 - loss: 1.9919 - val_accuracy: 0.3666 - val_loss: 1.5537
Epoch 6/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2891 - loss: 1.8905
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3057 - loss: 1.8879 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3087 - loss: 1.8873
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3108 - loss: 1.8862
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3116 - loss: 1.8856
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Epoch 7/92

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Epoch 8/92

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Epoch 9/92

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[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3353 - loss: 1.7465 
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[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3523 - loss: 1.7094
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3545 - loss: 1.7056 - val_accuracy: 0.4356 - val_loss: 1.4095
Epoch 10/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3731 - loss: 1.6895 
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3783 - loss: 1.6592
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3782 - loss: 1.6565
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3782 - loss: 1.6550
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3782 - loss: 1.6540 - val_accuracy: 0.4275 - val_loss: 1.4006
Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3992 - loss: 1.5711 
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3934 - loss: 1.5890
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Epoch 12/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4163 - loss: 1.5420 
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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4099 - loss: 1.5554
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Epoch 13/92

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Epoch 14/92

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[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4273 - loss: 1.5019
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Epoch 15/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4638 - loss: 1.4414 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4527 - loss: 1.4442
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4518 - loss: 1.4439
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4509 - loss: 1.4441
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4498 - loss: 1.4450 - val_accuracy: 0.4526 - val_loss: 1.3614
Epoch 16/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4576 - loss: 1.4051 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4553 - loss: 1.4051
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4545 - loss: 1.4073
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4539 - loss: 1.4101
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4527 - loss: 1.4145 - val_accuracy: 0.4644 - val_loss: 1.3507
Epoch 17/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4315 - loss: 1.4012 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4386 - loss: 1.4031
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4458 - loss: 1.4015
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[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4487 - loss: 1.4005
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Epoch 18/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4706 - loss: 1.3943 
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4630 - loss: 1.3923
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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4623 - loss: 1.3892
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4621 - loss: 1.3882 - val_accuracy: 0.4449 - val_loss: 1.3893
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.5000 - loss: 1.3579
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4625 - loss: 1.3922 
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4685 - loss: 1.3792
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4691 - loss: 1.3768
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4692 - loss: 1.3754
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4692 - loss: 1.3739
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Epoch 20/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4962 - loss: 1.3265 
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4877 - loss: 1.3366
[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4864 - loss: 1.3376
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4854 - loss: 1.3373
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4849 - loss: 1.3369
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 3ms/step - accuracy: 0.4848 - loss: 1.3368 - val_accuracy: 0.4538 - val_loss: 1.3865
Epoch 21/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5078 - loss: 1.2910
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4874 - loss: 1.3066 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4863 - loss: 1.3070
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4869 - loss: 1.3076
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4871 - loss: 1.3087
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4868 - loss: 1.3102 - val_accuracy: 0.4632 - val_loss: 1.3865

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16[0m 1s/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 814us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 21: 45.9 [%]
F1-score capturado en la ejecución 21: 44.81 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 804us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 61/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 839us/step
[1m124/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 821us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 46.3 [%]
Global F1 score (validation) = 45.67 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.6707831e-04 1.2636435e-03 1.0097113e-03 ... 5.6305842e-04
  4.4273003e-04 1.5196700e-04]
 [2.7341798e-03 4.8998082e-03 2.8538629e-03 ... 7.4073044e-04
  1.5596875e-03 2.6937653e-04]
 [5.9910060e-04 1.2108611e-03 5.0399889e-04 ... 4.2401865e-04
  8.5125002e-04 1.1058893e-04]
 ...
 [9.0838248e-06 5.4190764e-06 3.5508608e-06 ... 1.0027702e-03
  6.9598213e-04 6.9038884e-04]
 [1.0069985e-05 5.8800592e-06 4.1550534e-06 ... 1.6264810e-03
  6.2999508e-04 1.1922540e-03]
 [3.4763062e-04 1.8141203e-04 3.0469612e-04 ... 1.3274066e-01
  1.5507976e-03 2.3718392e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 59.11 [%]
Global accuracy score (test) = 44.82 [%]
Global F1 score (train) = 58.14 [%]
Global F1 score (test) = 43.4 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.25      0.22       161
 CAMINAR CON MÓVIL O LIBRO       0.21      0.24      0.23       161
       CAMINAR USUAL SPEED       0.26      0.26      0.26       161
            CAMINAR ZIGZAG       0.21      0.14      0.17       161
          DE PIE BARRIENDO       0.44      0.42      0.43       161
   DE PIE DOBLANDO TOALLAS       0.34      0.39      0.37       161
    DE PIE MOVIENDO LIBROS       0.35      0.32      0.33       161
          DE PIE USANDO PC       0.73      0.81      0.77       161
        FASE REPOSO CON K5       0.63      0.86      0.73       161
INCREMENTAL CICLOERGOMETRO       0.97      0.92      0.94       161
           SENTADO LEYENDO       0.32      0.64      0.42       161
         SENTADO USANDO PC       0.30      0.12      0.17       161
      SENTADO VIENDO LA TV       0.53      0.05      0.09       161
   SUBIR Y BAJAR ESCALERAS       0.50      0.60      0.55       161
                    TROTAR       0.94      0.75      0.83       138

                  accuracy                           0.45      2392
                 macro avg       0.46      0.45      0.43      2392
              weighted avg       0.46      0.45      0.43      2392

2025-10-27 12:40:51.729438: 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`.
2025-10-27 12:40:51.741997: 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 00:00:1761565251.755726  733385 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565251.760077  733385 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565251.770969  733385 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565251.770994  733385 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565251.770997  733385 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565251.770999  733385 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:40:51.774378: 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 00:00:1761565254.168799  733385 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12391 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565256.765739  733517 service.cc:152] XLA service 0x7ccc280261e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565256.765779  733517 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:40:56.820132: 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 00:00:1761565257.127837  733517 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565261.052497  733517 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:51[0m 6s/step - accuracy: 0.0234 - loss: 3.2288
[1m 19/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0713 - loss: 3.1473  
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0835 - loss: 3.0873
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0975 - loss: 3.0055
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1027 - loss: 2.9757
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1069 - loss: 2.95012025-10-27 12:41:02.715915: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:41:05.639440: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1104 - loss: 2.92962025-10-27 12:41:07.851709: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 54ms/step - accuracy: 0.1106 - loss: 2.9286 - val_accuracy: 0.2632 - val_loss: 2.0824
Epoch 2/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1771 - loss: 2.5939 
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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1836 - loss: 2.5186
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1852 - loss: 2.5072
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1865 - loss: 2.4978 - val_accuracy: 0.2858 - val_loss: 1.7815
Epoch 3/92

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Epoch 4/92

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Epoch 5/92

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[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2866 - loss: 1.9718
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Epoch 6/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3246 - loss: 1.8950 
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Epoch 7/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3035 - loss: 1.8424 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3116 - loss: 1.8270
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3187 - loss: 1.8118
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3211 - loss: 1.8081
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Epoch 8/92

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Epoch 9/92

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Epoch 10/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3887 - loss: 1.6437
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Epoch 11/92

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[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.3956 - loss: 1.5903
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Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4038 - loss: 1.5624 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4073 - loss: 1.5563
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[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4078 - loss: 1.5573
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Epoch 13/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4184 - loss: 1.5033 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4153 - loss: 1.5139
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4159 - loss: 1.5119
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4167 - loss: 1.5105
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4174 - loss: 1.5096
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4178 - loss: 1.5091 - val_accuracy: 0.4397 - val_loss: 1.3710
Epoch 14/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4282 - loss: 1.4782 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4328 - loss: 1.4802
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4347 - loss: 1.4808
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4352 - loss: 1.4810
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4356 - loss: 1.4805
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4357 - loss: 1.4806
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Epoch 15/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4288 - loss: 1.4802 
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4390 - loss: 1.4629
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4392 - loss: 1.4612
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Epoch 16/92

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Epoch 17/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4655 - loss: 1.3743 
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4570 - loss: 1.3960
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[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4562 - loss: 1.4008
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4562 - loss: 1.4009 - val_accuracy: 0.4704 - val_loss: 1.3427
Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4650 - loss: 1.3923 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4647 - loss: 1.3887
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4613 - loss: 1.3905
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4610 - loss: 1.3903
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4609 - loss: 1.3897
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4609 - loss: 1.3893 - val_accuracy: 0.4715 - val_loss: 1.3550
Epoch 19/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4637 - loss: 1.3541 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4632 - loss: 1.3526
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4636 - loss: 1.3527
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4641 - loss: 1.3545
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4646 - loss: 1.3549
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4652 - loss: 1.3548
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4653 - loss: 1.3554 - val_accuracy: 0.4583 - val_loss: 1.3716
Epoch 20/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4868 - loss: 1.3577 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4801 - loss: 1.3544
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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4772 - loss: 1.3527
[1m107/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4770 - loss: 1.3522
[1m126/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4766 - loss: 1.3518
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4765 - loss: 1.3512 - val_accuracy: 0.4692 - val_loss: 1.3667
Epoch 21/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5028 - loss: 1.3111 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4929 - loss: 1.3188
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4874 - loss: 1.3246
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4867 - loss: 1.3248
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4861 - loss: 1.3248
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4858 - loss: 1.3245 - val_accuracy: 0.4688 - val_loss: 1.3665

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 1s/step
[1m55/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 928us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 22: 44.82 [%]
F1-score capturado en la ejecución 22: 43.4 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10:49[0m 1s/step
[1m 60/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 858us/step
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[1m189/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 802us/step
[1m249/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 812us/step
[1m312/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 808us/step
[1m378/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 801us/step
[1m437/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 809us/step
[1m501/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 806us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m62/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 829us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 807us/step
[1m131/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 772us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 46.88 [%]
Global F1 score (validation) = 44.19 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.0743734e-04 8.1261585e-04 4.3088140e-04 ... 3.4960388e-04
  7.1065943e-04 2.0990062e-04]
 [2.4906308e-03 2.6463899e-03 1.5123256e-03 ... 9.8344020e-04
  4.7810790e-03 4.9716537e-04]
 [1.2097828e-03 1.5266973e-03 7.8787224e-04 ... 4.8643159e-04
  2.4832909e-03 2.1998733e-04]
 ...
 [3.0925003e-06 3.4929144e-06 2.7264191e-06 ... 9.1035222e-04
  6.0209294e-04 5.9893396e-04]
 [2.6181281e-06 2.8261022e-06 2.3801640e-06 ... 1.0469626e-03
  3.9932120e-04 8.9610450e-04]
 [4.6007571e-04 8.0947432e-04 4.3070814e-04 ... 2.8216311e-01
  5.9399125e-03 2.6025956e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.58 [%]
Global accuracy score (test) = 45.07 [%]
Global F1 score (train) = 53.79 [%]
Global F1 score (test) = 42.57 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.32      0.27       161
 CAMINAR CON MÓVIL O LIBRO       0.20      0.22      0.21       161
       CAMINAR USUAL SPEED       0.29      0.32      0.30       161
            CAMINAR ZIGZAG       0.27      0.07      0.12       161
          DE PIE BARRIENDO       0.41      0.42      0.42       161
   DE PIE DOBLANDO TOALLAS       0.39      0.27      0.32       161
    DE PIE MOVIENDO LIBROS       0.41      0.57      0.47       161
          DE PIE USANDO PC       0.81      0.83      0.82       161
        FASE REPOSO CON K5       0.51      0.88      0.64       161
INCREMENTAL CICLOERGOMETRO       0.99      0.94      0.96       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.85      0.07      0.13       161
      SENTADO VIENDO LA TV       0.21      0.45      0.29       161
   SUBIR Y BAJAR ESCALERAS       0.51      0.69      0.59       161
                    TROTAR       0.96      0.78      0.86       138

                  accuracy                           0.45      2392
                 macro avg       0.47      0.45      0.43      2392
              weighted avg       0.46      0.45      0.42      2392

2025-10-27 12:41:32.758711: 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`.
2025-10-27 12:41:32.771370: 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 00:00:1761565292.784996  736790 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565292.789322  736790 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565292.800426  736790 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565292.800451  736790 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565292.800454  736790 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565292.800457  736790 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:41:32.803732: 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 00:00:1761565295.193335  736790 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12391 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565297.757423  736917 service.cc:152] XLA service 0x77f060014210 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565297.757509  736917 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:41:37.811436: 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 00:00:1761565298.105476  736917 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565302.068830  736917 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:51[0m 6s/step - accuracy: 0.0781 - loss: 3.0747
[1m 18/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0750 - loss: 3.1173  
[1m 37/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0869 - loss: 3.0572
[1m 54/137[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0950 - loss: 3.0184
[1m 73/137[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1008 - loss: 2.9878
[1m 93/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1059 - loss: 2.9609
[1m112/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1098 - loss: 2.9387
[1m131/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.1131 - loss: 2.91862025-10-27 12:41:44.006258: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:41:46.723257: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1141 - loss: 2.91262025-10-27 12:41:48.825902: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1142 - loss: 2.9116 - val_accuracy: 0.2065 - val_loss: 2.1293
Epoch 2/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1016 - loss: 2.5698
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1540 - loss: 2.5275 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1594 - loss: 2.5340
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1640 - loss: 2.5268
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1671 - loss: 2.5190
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1696 - loss: 2.5105
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1719 - loss: 2.5021
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1738 - loss: 2.4947 - val_accuracy: 0.2822 - val_loss: 1.8636
Epoch 3/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1641 - loss: 2.3288
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2068 - loss: 2.3022 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2111 - loss: 2.2913
[1m 65/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2121 - loss: 2.2859
[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2134 - loss: 2.2798
[1m108/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2154 - loss: 2.2714
[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2171 - loss: 2.2643
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2179 - loss: 2.2608 - val_accuracy: 0.3229 - val_loss: 1.7207
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.0286
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2645 - loss: 2.0853 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2611 - loss: 2.0867
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2615 - loss: 2.0824
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2616 - loss: 2.0812
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2614 - loss: 2.0798
[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2617 - loss: 2.0773
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2621 - loss: 2.0758 - val_accuracy: 0.3569 - val_loss: 1.5854
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.1694
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2797 - loss: 2.0212 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2833 - loss: 2.0046
[1m 65/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2856 - loss: 1.9934
[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2866 - loss: 1.9880
[1m107/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2872 - loss: 1.9838
[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2880 - loss: 1.9801
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2885 - loss: 1.9782 - val_accuracy: 0.3779 - val_loss: 1.5408
Epoch 6/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2891 - loss: 2.1225
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2864 - loss: 1.9580 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2931 - loss: 1.9382
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2975 - loss: 1.9185
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Epoch 7/92

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[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3161 - loss: 1.8289
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Epoch 8/92

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Epoch 9/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3551 - loss: 1.7045 
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3626 - loss: 1.7027
[1m108/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3623 - loss: 1.7028
[1m133/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3618 - loss: 1.7026
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3618 - loss: 1.7024 - val_accuracy: 0.4316 - val_loss: 1.4055
Epoch 10/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3670 - loss: 1.6550 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3690 - loss: 1.6614
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3717 - loss: 1.6621
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3732 - loss: 1.6608
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3749 - loss: 1.6593 - val_accuracy: 0.4362 - val_loss: 1.3828
Epoch 11/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3886 - loss: 1.6296 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3867 - loss: 1.6231
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3850 - loss: 1.6277
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3854 - loss: 1.6265
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3857 - loss: 1.6244
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Epoch 12/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3811 - loss: 1.5896 
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Epoch 13/92

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Epoch 14/92

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[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4339 - loss: 1.4896
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Epoch 15/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4341 - loss: 1.4689 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4384 - loss: 1.4657
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[1m 89/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4368 - loss: 1.4695
[1m111/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4365 - loss: 1.4695
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4360 - loss: 1.4696 - val_accuracy: 0.4581 - val_loss: 1.3508
Epoch 16/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4317 - loss: 1.4852 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4372 - loss: 1.4686
[1m 65/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4405 - loss: 1.4596
[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4421 - loss: 1.4544
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4428 - loss: 1.4508
[1m129/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4432 - loss: 1.4481
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4432 - loss: 1.4475 - val_accuracy: 0.4429 - val_loss: 1.3535
Epoch 17/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4636 - loss: 1.3717 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4586 - loss: 1.3859
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[1m 87/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4574 - loss: 1.3946
[1m108/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4568 - loss: 1.3971
[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4561 - loss: 1.3990
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Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4675 - loss: 1.3865 
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4618 - loss: 1.3945
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4611 - loss: 1.3956
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4608 - loss: 1.3951
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4605 - loss: 1.3945 - val_accuracy: 0.4654 - val_loss: 1.3533
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.4766 - loss: 1.3775
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4719 - loss: 1.3519 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4714 - loss: 1.3518
[1m 68/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4692 - loss: 1.3560
[1m 91/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4671 - loss: 1.3602
[1m112/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4655 - loss: 1.3640
[1m133/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4646 - loss: 1.3659
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4644 - loss: 1.3662 - val_accuracy: 0.4721 - val_loss: 1.3573
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4766 - loss: 1.5033
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4824 - loss: 1.3819 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4806 - loss: 1.3709
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4799 - loss: 1.3633
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4794 - loss: 1.3577
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4789 - loss: 1.3553
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4785 - loss: 1.3534
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4779 - loss: 1.3527 - val_accuracy: 0.4605 - val_loss: 1.3566

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 1s/step
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 848us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 23: 45.07 [%]
F1-score capturado en la ejecución 23: 42.57 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:23[0m 1s/step
[1m 59/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 867us/step
[1m119/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 853us/step
[1m184/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 824us/step
[1m246/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 824us/step
[1m307/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 825us/step
[1m370/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 821us/step
[1m435/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 815us/step
[1m497/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 815us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 815us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 64/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 798us/step
[1m121/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 843us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 46.05 [%]
Global F1 score (validation) = 44.45 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.9073023e-04 1.2549384e-03 5.0469977e-04 ... 2.3210386e-04
  4.7506407e-04 2.5665548e-04]
 [2.6482986e-03 5.0614090e-03 2.3517578e-03 ... 6.1333150e-04
  1.6211104e-03 4.5435081e-04]
 [6.3295692e-04 1.1230923e-03 6.3436432e-04 ... 2.6291629e-04
  9.5060864e-04 1.5597057e-04]
 ...
 [1.2980568e-05 6.5875984e-06 1.1566978e-05 ... 1.8447490e-03
  1.0450712e-03 1.1552757e-03]
 [2.0250545e-05 1.0839924e-05 2.0599773e-05 ... 3.1927316e-03
  1.0029858e-03 2.0576566e-03]
 [8.4225187e-04 6.0555880e-04 9.9924882e-04 ... 3.0116400e-01
  1.1062364e-03 6.0896124e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.57 [%]
Global accuracy score (test) = 46.53 [%]
Global F1 score (train) = 55.39 [%]
Global F1 score (test) = 45.41 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.17      0.20       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.33      0.29       161
       CAMINAR USUAL SPEED       0.28      0.33      0.31       161
            CAMINAR ZIGZAG       0.17      0.11      0.13       161
          DE PIE BARRIENDO       0.43      0.50      0.46       161
   DE PIE DOBLANDO TOALLAS       0.35      0.22      0.27       161
    DE PIE MOVIENDO LIBROS       0.35      0.47      0.40       161
          DE PIE USANDO PC       0.79      0.80      0.79       161
        FASE REPOSO CON K5       0.53      0.88      0.66       161
INCREMENTAL CICLOERGOMETRO       0.98      0.92      0.95       161
           SENTADO LEYENDO       0.37      0.27      0.31       161
         SENTADO USANDO PC       0.46      0.44      0.45       161
      SENTADO VIENDO LA TV       0.23      0.12      0.16       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.73      0.62       161
                    TROTAR       0.89      0.73      0.80       138

                  accuracy                           0.47      2392
                 macro avg       0.46      0.47      0.45      2392
              weighted avg       0.45      0.47      0.45      2392

2025-10-27 12:42:13.560691: 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`.
2025-10-27 12:42:13.573578: 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 00:00:1761565333.587805  740149 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565333.592113  740149 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565333.603414  740149 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565333.603441  740149 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565333.603446  740149 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565333.603449  740149 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:42:13.606870: 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 00:00:1761565336.109108  740149 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12271 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565338.770064  740310 service.cc:152] XLA service 0x7fa70c0133e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565338.770142  740310 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:42:18.829381: 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 00:00:1761565339.130661  740310 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565343.274839  740310 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:26[0m 6s/step - accuracy: 0.0859 - loss: 3.0995
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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1095 - loss: 2.93072025-10-27 12:42:25.122915: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:42:28.184744: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 36ms/step - accuracy: 0.1125 - loss: 2.91332025-10-27 12:42:30.425150: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 56ms/step - accuracy: 0.1127 - loss: 2.9124 - val_accuracy: 0.2136 - val_loss: 2.1888
Epoch 2/92

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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1805 - loss: 2.5130
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Epoch 3/92

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[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2201 - loss: 2.2699
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2208 - loss: 2.2657 - val_accuracy: 0.3279 - val_loss: 1.7246
Epoch 4/92

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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2575 - loss: 2.0926
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2578 - loss: 2.0925
[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2584 - loss: 2.0914
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2588 - loss: 2.0903 - val_accuracy: 0.3385 - val_loss: 1.6462
Epoch 5/92

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[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2804 - loss: 1.9915
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Epoch 6/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3252 - loss: 1.8733 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 1.8786
[1m 67/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3155 - loss: 1.8813
[1m 89/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 1.8801
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Epoch 7/92

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Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3304 - loss: 1.7884 
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[1m 88/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3360 - loss: 1.7685
[1m111/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3368 - loss: 1.7667
[1m134/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3376 - loss: 1.7651
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3379 - loss: 1.7646 - val_accuracy: 0.4117 - val_loss: 1.4402
Epoch 9/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3681 - loss: 1.6992 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3682 - loss: 1.6893
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3680 - loss: 1.6889
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3671 - loss: 1.6899
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3663 - loss: 1.6904
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3660 - loss: 1.6905
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3658 - loss: 1.6908 - val_accuracy: 0.4269 - val_loss: 1.4143
Epoch 10/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3783 - loss: 1.6510 
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[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3801 - loss: 1.6474
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Epoch 11/92

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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3914 - loss: 1.6073
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Epoch 12/92

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[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4097 - loss: 1.5419
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4092 - loss: 1.5444
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Epoch 13/92

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Epoch 14/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4366 - loss: 1.4778 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4377 - loss: 1.4757
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4364 - loss: 1.4822
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4355 - loss: 1.4839
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4350 - loss: 1.4845
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4345 - loss: 1.4847 - val_accuracy: 0.4557 - val_loss: 1.3597
Epoch 15/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4414 - loss: 1.4481 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4452 - loss: 1.4513
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[1m 76/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4455 - loss: 1.4589
[1m 95/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4444 - loss: 1.4621
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4437 - loss: 1.4639
[1m134/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4432 - loss: 1.4647
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.4430 - loss: 1.4648 - val_accuracy: 0.4607 - val_loss: 1.3513
Epoch 16/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4388 - loss: 1.4799 
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[1m 96/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4387 - loss: 1.4623
[1m116/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4397 - loss: 1.4596
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Epoch 17/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4192 - loss: 1.4558 
[1m 37/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4253 - loss: 1.4531
[1m 56/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4283 - loss: 1.4520
[1m 76/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4310 - loss: 1.4488
[1m 95/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4330 - loss: 1.4455
[1m114/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4349 - loss: 1.4425
[1m133/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4365 - loss: 1.4395
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step - accuracy: 0.4369 - loss: 1.4388 - val_accuracy: 0.4565 - val_loss: 1.3639
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.4531 - loss: 1.4289
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4437 - loss: 1.4015 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4460 - loss: 1.4071
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4476 - loss: 1.4071
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4497 - loss: 1.4025
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4516 - loss: 1.3990
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4528 - loss: 1.3966
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4537 - loss: 1.3950 - val_accuracy: 0.4581 - val_loss: 1.3512
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.4001
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4570 - loss: 1.3801 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4548 - loss: 1.3849
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4557 - loss: 1.3821
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4572 - loss: 1.3791
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4593 - loss: 1.3765
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4604 - loss: 1.3754
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4610 - loss: 1.3747 - val_accuracy: 0.4650 - val_loss: 1.3668
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4922 - loss: 1.2264
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4766 - loss: 1.3354 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4735 - loss: 1.3462
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4741 - loss: 1.3476
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4747 - loss: 1.3480
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4754 - loss: 1.3472
[1m127/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4752 - loss: 1.3473
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4751 - loss: 1.3474 - val_accuracy: 0.4700 - val_loss: 1.3744
Epoch 21/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4531 - loss: 1.4678
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4706 - loss: 1.3402 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4750 - loss: 1.3420
[1m 67/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4777 - loss: 1.3390
[1m 90/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4796 - loss: 1.3371
[1m112/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.3350
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4823 - loss: 1.3329
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4825 - loss: 1.3327 - val_accuracy: 0.4640 - val_loss: 1.3732

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:26[0m 1s/step
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 808us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 24: 46.53 [%]
F1-score capturado en la ejecución 24: 45.41 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:56[0m 1s/step
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[1m174/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 876us/step
[1m232/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 875us/step
[1m291/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 874us/step
[1m354/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 860us/step
[1m418/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 850us/step
[1m483/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 840us/step
[1m546/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 835us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 795us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 857us/step
[1m123/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 830us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 46.4 [%]
Global F1 score (validation) = 45.61 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.1788671e-03 2.0334844e-03 1.5240294e-03 ... 2.9905114e-04
  1.0106851e-03 7.9400802e-04]
 [3.7167252e-03 5.1658195e-03 4.5581367e-03 ... 1.3536873e-03
  2.0906460e-03 6.4232422e-04]
 [7.1593968e-04 1.1992255e-03 8.6398027e-04 ... 6.4822932e-04
  7.5796101e-04 2.8524513e-04]
 ...
 [8.3085779e-06 5.0082836e-06 4.1526391e-06 ... 1.3734292e-03
  6.0483493e-04 5.1886088e-04]
 [1.5904281e-05 8.5834163e-06 8.5208512e-06 ... 3.2640097e-03
  6.2969670e-04 9.8817609e-04]
 [5.4818857e-04 2.1611534e-04 3.5342379e-04 ... 2.7642789e-01
  6.6103326e-04 3.4469161e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 57.59 [%]
Global accuracy score (test) = 46.07 [%]
Global F1 score (train) = 56.78 [%]
Global F1 score (test) = 45.2 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.12      0.16       161
 CAMINAR CON MÓVIL O LIBRO       0.24      0.33      0.28       161
       CAMINAR USUAL SPEED       0.23      0.35      0.28       161
            CAMINAR ZIGZAG       0.24      0.14      0.18       161
          DE PIE BARRIENDO       0.45      0.46      0.45       161
   DE PIE DOBLANDO TOALLAS       0.28      0.23      0.25       161
    DE PIE MOVIENDO LIBROS       0.37      0.43      0.40       161
          DE PIE USANDO PC       0.75      0.83      0.79       161
        FASE REPOSO CON K5       0.58      0.86      0.70       161
INCREMENTAL CICLOERGOMETRO       0.97      0.93      0.95       161
           SENTADO LEYENDO       0.30      0.34      0.32       161
         SENTADO USANDO PC       0.35      0.17      0.23       161
      SENTADO VIENDO LA TV       0.40      0.30      0.34       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.68      0.60       161
                    TROTAR       0.95      0.76      0.84       138

                  accuracy                           0.46      2392
                 macro avg       0.46      0.46      0.45      2392
              weighted avg       0.46      0.46      0.45      2392

2025-10-27 12:42:55.959769: 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`.
2025-10-27 12:42:55.972792: 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 00:00:1761565375.986887  743781 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565375.991355  743781 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565376.002544  743781 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565376.002571  743781 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565376.002574  743781 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565376.002577  743781 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:42:56.006100: 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 00:00:1761565378.497062  743781 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12248 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565381.131685  743925 service.cc:152] XLA service 0x7bf250003020 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565381.131726  743925 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:43:01.193151: 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 00:00:1761565381.518127  743925 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565385.691716  743925 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:33[0m 6s/step - accuracy: 0.0391 - loss: 3.2511
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0756 - loss: 3.1216  
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0902 - loss: 3.0531
[1m 67/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0993 - loss: 3.0076
[1m 88/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1050 - loss: 2.9756
[1m110/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1100 - loss: 2.9481
[1m132/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1142 - loss: 2.92362025-10-27 12:43:07.718292: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:43:10.375402: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 35ms/step - accuracy: 0.1150 - loss: 2.91842025-10-27 12:43:12.531502: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 55ms/step - accuracy: 0.1152 - loss: 2.9174 - val_accuracy: 0.2235 - val_loss: 2.1010
Epoch 2/92

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[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1762 - loss: 2.5220
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1783 - loss: 2.5098
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Epoch 3/92

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[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2291 - loss: 2.2574
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Epoch 4/92

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[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2696 - loss: 2.0914
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Epoch 5/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2704 - loss: 2.0134 
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2815 - loss: 1.9882
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2831 - loss: 1.9835
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Epoch 6/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3069 - loss: 1.8890 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3124 - loss: 1.8892
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3129 - loss: 1.8900
[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3122 - loss: 1.8910
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3119 - loss: 1.8896
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3120 - loss: 1.8875
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3120 - loss: 1.8857
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3120 - loss: 1.8855 - val_accuracy: 0.3804 - val_loss: 1.5002
Epoch 7/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3371 - loss: 1.8241 
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[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3332 - loss: 1.8198
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Epoch 8/92

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[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3458 - loss: 1.7506
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3438 - loss: 1.7530
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3429 - loss: 1.7530
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Epoch 9/92

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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3662 - loss: 1.6888
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Epoch 10/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3904 - loss: 1.6214
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3901 - loss: 1.6232
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3898 - loss: 1.6252
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3895 - loss: 1.6269 - val_accuracy: 0.4385 - val_loss: 1.3830
Epoch 11/92

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[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3883 - loss: 1.6216
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[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3897 - loss: 1.6137
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3902 - loss: 1.6106
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3911 - loss: 1.6073
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3917 - loss: 1.6053 - val_accuracy: 0.4364 - val_loss: 1.3683
Epoch 12/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4229 - loss: 1.5562 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4228 - loss: 1.5547
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4219 - loss: 1.5537
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4204 - loss: 1.5529
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4188 - loss: 1.5533
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4176 - loss: 1.5534
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4167 - loss: 1.5533 - val_accuracy: 0.4498 - val_loss: 1.3554
Epoch 13/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4020 - loss: 1.5677 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4070 - loss: 1.5552
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4095 - loss: 1.5468
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4115 - loss: 1.5409
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4131 - loss: 1.5358
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4136 - loss: 1.5336
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Epoch 14/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4309 - loss: 1.4909
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Epoch 15/92

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Epoch 16/92

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Epoch 17/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4426 - loss: 1.3984 
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Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4903 - loss: 1.3536 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4725 - loss: 1.3789
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4708 - loss: 1.3823
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4690 - loss: 1.3847
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Epoch 19/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4689 - loss: 1.3670 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4645 - loss: 1.3701
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4642 - loss: 1.3695
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Epoch 20/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4564 - loss: 1.3587 
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Epoch 21/92

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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4744 - loss: 1.3485
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16[0m 1s/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 901us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 25: 46.07 [%]
F1-score capturado en la ejecución 25: 45.2 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 57/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 893us/step
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[1m185/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 822us/step
[1m249/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 813us/step
[1m313/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 809us/step
[1m371/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 818us/step
[1m432/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 820us/step
[1m496/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 816us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 892us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 64/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 799us/step
[1m127/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 799us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 45.36 [%]
Global F1 score (validation) = 43.02 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.4966531e-04 5.9392408e-04 4.7769980e-04 ... 2.9771915e-04
  1.9308776e-04 2.0220838e-04]
 [2.0579938e-03 3.0093784e-03 2.3556310e-03 ... 8.7677903e-04
  1.9050619e-03 4.5329434e-04]
 [6.8533141e-04 9.3400240e-04 7.0345140e-04 ... 5.3421885e-04
  7.6852570e-04 1.8500480e-04]
 ...
 [1.4409742e-05 1.3101102e-05 9.4770712e-06 ... 1.8513489e-03
  7.3296257e-04 1.3185082e-03]
 [1.7687747e-05 1.5439036e-05 1.1750711e-05 ... 1.6967619e-03
  5.4074021e-04 1.3745181e-03]
 [4.5547125e-04 3.8422196e-04 4.2014374e-04 ... 2.8863060e-01
  6.6767127e-04 3.1696486e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.65 [%]
Global accuracy score (test) = 48.24 [%]
Global F1 score (train) = 54.91 [%]
Global F1 score (test) = 46.43 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.19      0.09      0.12       161
 CAMINAR CON MÓVIL O LIBRO       0.24      0.30      0.27       161
       CAMINAR USUAL SPEED       0.26      0.34      0.29       161
            CAMINAR ZIGZAG       0.23      0.19      0.21       161
          DE PIE BARRIENDO       0.47      0.45      0.46       161
   DE PIE DOBLANDO TOALLAS       0.29      0.09      0.14       161
    DE PIE MOVIENDO LIBROS       0.37      0.63      0.46       161
          DE PIE USANDO PC       0.74      0.81      0.78       161
        FASE REPOSO CON K5       0.64      0.86      0.73       161
INCREMENTAL CICLOERGOMETRO       0.94      0.94      0.94       161
           SENTADO LEYENDO       0.41      0.43      0.42       161
         SENTADO USANDO PC       0.50      0.53      0.52       161
      SENTADO VIENDO LA TV       0.28      0.12      0.17       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.70      0.60       161
                    TROTAR       0.92      0.80      0.86       138

                  accuracy                           0.48      2392
                 macro avg       0.47      0.49      0.46      2392
              weighted avg       0.46      0.48      0.46      2392

2025-10-27 12:43:37.624764: 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`.
2025-10-27 12:43:37.637336: 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 00:00:1761565417.651054  747177 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565417.655219  747177 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565417.666355  747177 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565417.666378  747177 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565417.666381  747177 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565417.666383  747177 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:43:37.669733: 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 00:00:1761565420.077631  747177 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12261 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565422.691212  747333 service.cc:152] XLA service 0x7f6ea4024660 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565422.691292  747333 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:43:42.748744: 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 00:00:1761565423.043194  747333 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565427.008962  747333 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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2025-10-27 12:43:51.707478: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


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
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Epoch 2/92

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Epoch 3/92

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Epoch 4/92

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Epoch 5/92

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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2737 - loss: 2.0091
[1m106/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2749 - loss: 2.0054
[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2764 - loss: 2.0011
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Epoch 6/92

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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2904 - loss: 1.9083
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[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2927 - loss: 1.9025
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Epoch 7/92

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[1m 24/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3176 - loss: 1.8084 
[1m 45/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 1.8008
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[1m 88/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3269 - loss: 1.8012
[1m110/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3277 - loss: 1.8005
[1m132/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3284 - loss: 1.7996
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3287 - loss: 1.7991 - val_accuracy: 0.4221 - val_loss: 1.4442
Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3447 - loss: 1.7390 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3457 - loss: 1.7433
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[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3447 - loss: 1.7475
[1m109/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3443 - loss: 1.7485
[1m134/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3445 - loss: 1.7469
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3446 - loss: 1.7466 - val_accuracy: 0.4243 - val_loss: 1.4258
Epoch 9/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3645 - loss: 1.6740 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3651 - loss: 1.6893
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3670 - loss: 1.6880
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3677 - loss: 1.6883
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3679 - loss: 1.6891
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3682 - loss: 1.6887
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3686 - loss: 1.6876 - val_accuracy: 0.4144 - val_loss: 1.4202
Epoch 10/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3928 - loss: 1.6125 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3912 - loss: 1.6286
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3890 - loss: 1.6340
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3871 - loss: 1.6383
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3865 - loss: 1.6391
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3861 - loss: 1.6391
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3859 - loss: 1.6389 - val_accuracy: 0.4405 - val_loss: 1.3935
Epoch 11/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3951 - loss: 1.5899 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3917 - loss: 1.5995
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3936 - loss: 1.5975
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3946 - loss: 1.5954
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Epoch 12/92

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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4020 - loss: 1.5530
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Epoch 13/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4118 - loss: 1.5295
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Epoch 14/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4220 - loss: 1.4634 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4249 - loss: 1.4716
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4268 - loss: 1.4768
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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4276 - loss: 1.4774
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4282 - loss: 1.4769 - val_accuracy: 0.4684 - val_loss: 1.3491
Epoch 15/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4360 - loss: 1.4706 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4355 - loss: 1.4699
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4340 - loss: 1.4699
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4339 - loss: 1.4680
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[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4345 - loss: 1.4649
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4351 - loss: 1.4636 - val_accuracy: 0.4634 - val_loss: 1.3526
Epoch 16/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4543 - loss: 1.4067 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4501 - loss: 1.4136
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4476 - loss: 1.4204
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Epoch 17/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4353 - loss: 1.4348 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4416 - loss: 1.4133
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4432 - loss: 1.4110
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4442 - loss: 1.4098
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4447 - loss: 1.4091 - val_accuracy: 0.4613 - val_loss: 1.3499
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.4219 - loss: 1.3829
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4476 - loss: 1.3722 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4554 - loss: 1.3694
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4570 - loss: 1.3724
[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4575 - loss: 1.3729
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4576 - loss: 1.3731
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4577 - loss: 1.3734
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4577 - loss: 1.3736 - val_accuracy: 0.4634 - val_loss: 1.3546
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4766 - loss: 1.3682
[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4619 - loss: 1.3690 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4596 - loss: 1.3724
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4609 - loss: 1.3699
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4618 - loss: 1.3674
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4621 - loss: 1.3660
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4623 - loss: 1.3652
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4627 - loss: 1.3646 - val_accuracy: 0.4561 - val_loss: 1.3939

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16[0m 1s/step
[1m53/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 977us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 26: 48.24 [%]
F1-score capturado en la ejecución 26: 46.43 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10:53[0m 1s/step
[1m 62/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 827us/step
[1m124/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 817us/step
[1m184/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 826us/step
[1m250/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 808us/step
[1m313/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 806us/step
[1m380/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 796us/step
[1m435/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 811us/step
[1m491/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 820us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 797us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 57/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 896us/step
[1m123/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 824us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
Global accuracy score (validation) = 45.61 [%]
Global F1 score (validation) = 44.05 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.96517415e-04 1.53423916e-03 9.05597059e-04 ... 3.02591274e-04
  8.82013002e-04 1.16651929e-04]
 [4.36431216e-03 1.10305510e-02 6.23967359e-03 ... 2.46185972e-03
  7.02539552e-03 6.21117826e-04]
 [6.99024589e-04 1.76481146e-03 1.04552496e-03 ... 1.18438748e-03
  1.40710606e-03 1.19477809e-04]
 ...
 [3.48547946e-05 1.61129046e-05 1.73355238e-05 ... 5.06931264e-03
  3.56184755e-04 2.62938300e-03]
 [2.57615357e-05 1.24807475e-05 1.23630534e-05 ... 3.28696915e-03
  3.05953668e-04 2.45853234e-03]
 [3.29826085e-04 2.26417906e-04 4.09735774e-04 ... 2.74980158e-01
  2.73439975e-04 2.15868652e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 54.98 [%]
Global accuracy score (test) = 43.69 [%]
Global F1 score (train) = 53.56 [%]
Global F1 score (test) = 41.69 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.16      0.18       161
 CAMINAR CON MÓVIL O LIBRO       0.19      0.13      0.15       161
       CAMINAR USUAL SPEED       0.25      0.46      0.32       161
            CAMINAR ZIGZAG       0.09      0.04      0.06       161
          DE PIE BARRIENDO       0.40      0.34      0.37       161
   DE PIE DOBLANDO TOALLAS       0.31      0.17      0.22       161
    DE PIE MOVIENDO LIBROS       0.32      0.48      0.39       161
          DE PIE USANDO PC       0.70      0.84      0.76       161
        FASE REPOSO CON K5       0.50      0.88      0.64       161
INCREMENTAL CICLOERGOMETRO       0.96      0.93      0.94       161
           SENTADO LEYENDO       0.43      0.35      0.38       161
         SENTADO USANDO PC       0.36      0.17      0.24       161
      SENTADO VIENDO LA TV       0.15      0.12      0.14       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.73      0.62       161
                    TROTAR       0.92      0.80      0.85       138

                  accuracy                           0.44      2392
                 macro avg       0.42      0.44      0.42      2392
              weighted avg       0.42      0.44      0.41      2392

2025-10-27 12:44:17.678361: 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`.
2025-10-27 12:44:17.690858: 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 00:00:1761565457.704698  750387 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565457.708890  750387 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565457.719927  750387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565457.719979  750387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565457.719983  750387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565457.719986  750387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:44:17.723257: 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 00:00:1761565460.099115  750387 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12272 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565462.686940  750541 service.cc:152] XLA service 0x7e51100050d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565462.687029  750541 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:44:22.753269: 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 00:00:1761565463.065356  750541 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565466.978357  750541 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:51[0m 6s/step - accuracy: 0.0938 - loss: 3.0486
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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1133 - loss: 2.91152025-10-27 12:44:28.979860: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:44:31.615328: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


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
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1164 - loss: 2.8937 - val_accuracy: 0.2053 - val_loss: 2.1349
Epoch 2/92

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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1683 - loss: 2.5386
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Epoch 3/92

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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2187 - loss: 2.2790
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2207 - loss: 2.2736
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2221 - loss: 2.2688 - val_accuracy: 0.3156 - val_loss: 1.7062
Epoch 4/92

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[1m133/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.2657 - loss: 2.0863
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2657 - loss: 2.0857 - val_accuracy: 0.3589 - val_loss: 1.5872
Epoch 5/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2764 - loss: 1.9948
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2781 - loss: 1.9898
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Epoch 6/92

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[1m 76/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2966 - loss: 1.9322
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[1m113/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3008 - loss: 1.9160
[1m133/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3023 - loss: 1.9098
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3027 - loss: 1.9084 - val_accuracy: 0.3901 - val_loss: 1.4801
Epoch 7/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3176 - loss: 1.8255 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3228 - loss: 1.8325
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3238 - loss: 1.8303
[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3247 - loss: 1.8276
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3254 - loss: 1.8250 - val_accuracy: 0.4164 - val_loss: 1.4371
Epoch 8/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3357 - loss: 1.7814 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3407 - loss: 1.7771
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3439 - loss: 1.7668
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3463 - loss: 1.7592
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3479 - loss: 1.7541
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3490 - loss: 1.7508
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3498 - loss: 1.7488 - val_accuracy: 0.4174 - val_loss: 1.4453
Epoch 9/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3435 - loss: 1.7273 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3485 - loss: 1.7252
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3525 - loss: 1.7197
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3560 - loss: 1.7157
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[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3608 - loss: 1.7087
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Epoch 10/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3852 - loss: 1.6320 
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Epoch 11/92

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Epoch 12/92

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[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4175 - loss: 1.5488
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Epoch 13/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4234 - loss: 1.4923 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4223 - loss: 1.5051
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4217 - loss: 1.5108
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4208 - loss: 1.5118
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4207 - loss: 1.5115 - val_accuracy: 0.4494 - val_loss: 1.3602
Epoch 14/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4040 - loss: 1.5200 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4114 - loss: 1.5164
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4151 - loss: 1.5090
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4163 - loss: 1.5064
[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4171 - loss: 1.5040
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4178 - loss: 1.5023 - val_accuracy: 0.4457 - val_loss: 1.3732
Epoch 15/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4355 - loss: 1.4802 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4375 - loss: 1.4706
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[1m 78/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4368 - loss: 1.4698
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4370 - loss: 1.4680
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4374 - loss: 1.4654
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Epoch 16/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4544 - loss: 1.4484 
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[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4489 - loss: 1.4333
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4481 - loss: 1.4320
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4477 - loss: 1.4318
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4473 - loss: 1.4315
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4469 - loss: 1.4313 - val_accuracy: 0.4609 - val_loss: 1.3538
Epoch 17/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4766 - loss: 1.3985
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4610 - loss: 1.3893 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4618 - loss: 1.3905
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4606 - loss: 1.3967
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4592 - loss: 1.3995
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4585 - loss: 1.4009
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4580 - loss: 1.4022
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4573 - loss: 1.4034 - val_accuracy: 0.4619 - val_loss: 1.3576
Epoch 18/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4766 - loss: 1.2375
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4562 - loss: 1.3602 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4565 - loss: 1.3674
[1m 64/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4568 - loss: 1.3720
[1m 85/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4567 - loss: 1.3759
[1m105/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4569 - loss: 1.3784
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4565 - loss: 1.3803
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4563 - loss: 1.3808 - val_accuracy: 0.4682 - val_loss: 1.3723
Epoch 19/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4531 - loss: 1.2655
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4656 - loss: 1.3564 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4691 - loss: 1.3565
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4705 - loss: 1.3578
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4702 - loss: 1.3590
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4692 - loss: 1.3616
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4691 - loss: 1.3621
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4690 - loss: 1.3622 - val_accuracy: 0.4648 - val_loss: 1.3596
Epoch 20/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5234 - loss: 1.1594
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4804 - loss: 1.3157 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4767 - loss: 1.3265
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4758 - loss: 1.3309
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4754 - loss: 1.3345
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4750 - loss: 1.3367
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4747 - loss: 1.3379
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4747 - loss: 1.3381 - val_accuracy: 0.4559 - val_loss: 1.3772
Epoch 21/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3984 - loss: 1.4820
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4571 - loss: 1.3450 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4646 - loss: 1.3324
[1m 59/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4696 - loss: 1.3273
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4713 - loss: 1.3259
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4722 - loss: 1.3261
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4729 - loss: 1.3260
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4734 - loss: 1.3260 - val_accuracy: 0.4664 - val_loss: 1.3826

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 1s/step
[1m55/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 929us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 27: 43.69 [%]
F1-score capturado en la ejecución 27: 41.69 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:00[0m 1s/step
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[1m172/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 882us/step
[1m232/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 871us/step
[1m288/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 878us/step
[1m349/547[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 869us/step
[1m407/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 868us/step
[1m458/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 881us/step
[1m513/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 885us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 886us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 55/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 932us/step
[1m120/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 851us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 46.64 [%]
Global F1 score (validation) = 44.17 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[9.7585894e-04 9.7847136e-04 6.6776056e-04 ... 2.8127796e-04
  4.3959686e-04 3.4381845e-04]
 [2.3755310e-03 3.6121174e-03 2.2605809e-03 ... 9.2045980e-04
  8.8950666e-04 5.4085068e-04]
 [1.0003421e-03 1.3534260e-03 9.6807914e-04 ... 4.9636723e-04
  6.1750569e-04 4.5573356e-04]
 ...
 [1.0691987e-05 7.1221816e-06 8.3185851e-06 ... 1.6293696e-03
  5.7457970e-04 1.1075455e-03]
 [1.1834545e-05 8.7322651e-06 9.1588263e-06 ... 1.3085789e-03
  6.1766664e-04 1.1717178e-03]
 [1.0024352e-03 5.4061163e-04 1.2648189e-03 ... 2.8426722e-01
  2.3458195e-03 6.9610556e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.04 [%]
Global accuracy score (test) = 43.77 [%]
Global F1 score (train) = 53.88 [%]
Global F1 score (test) = 41.39 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.22      0.21       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.25      0.25       161
       CAMINAR USUAL SPEED       0.24      0.35      0.29       161
            CAMINAR ZIGZAG       0.11      0.02      0.04       161
          DE PIE BARRIENDO       0.43      0.43      0.43       161
   DE PIE DOBLANDO TOALLAS       0.30      0.15      0.20       161
    DE PIE MOVIENDO LIBROS       0.32      0.47      0.38       161
          DE PIE USANDO PC       0.72      0.81      0.76       161
        FASE REPOSO CON K5       0.53      0.89      0.67       161
INCREMENTAL CICLOERGOMETRO       0.97      0.89      0.93       161
           SENTADO LEYENDO       0.36      0.51      0.42       161
         SENTADO USANDO PC       0.36      0.06      0.10       161
      SENTADO VIENDO LA TV       0.19      0.12      0.15       161
   SUBIR Y BAJAR ESCALERAS       0.45      0.67      0.54       161
                    TROTAR       0.95      0.78      0.85       138

                  accuracy                           0.44      2392
                 macro avg       0.43      0.44      0.41      2392
              weighted avg       0.42      0.44      0.41      2392

2025-10-27 12:44:58.721712: 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`.
2025-10-27 12:44:58.734134: 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 00:00:1761565498.747677  753791 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565498.751824  753791 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565498.762838  753791 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565498.762859  753791 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565498.762862  753791 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565498.762864  753791 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:44:58.766239: 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 00:00:1761565501.131119  753791 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12265 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565503.708775  753934 service.cc:152] XLA service 0x7f5ab8024430 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565503.708818  753934 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:45:03.761503: 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 00:00:1761565504.054849  753934 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565507.919371  753934 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:37[0m 6s/step - accuracy: 0.0547 - loss: 3.1241
[1m 18/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0714 - loss: 3.1068  
[1m 38/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0841 - loss: 3.0436
[1m 58/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0928 - loss: 3.0019
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0997 - loss: 2.9690
[1m 98/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1044 - loss: 2.9443
[1m117/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1082 - loss: 2.92352025-10-27 12:45:09.923703: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_3970', 160 bytes spill stores, 160 bytes spill loads

2025-10-27 12:45:12.571837: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.1120 - loss: 2.90382025-10-27 12:45:14.709884: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 54ms/step - accuracy: 0.1122 - loss: 2.9028 - val_accuracy: 0.2433 - val_loss: 2.0802
Epoch 2/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.1094 - loss: 2.7875
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1675 - loss: 2.6096 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1745 - loss: 2.5726
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1779 - loss: 2.5529
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1805 - loss: 2.5378
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1829 - loss: 2.5259
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1847 - loss: 2.5149
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1862 - loss: 2.5067 - val_accuracy: 0.2982 - val_loss: 1.8120
Epoch 3/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 29ms/step - accuracy: 0.1797 - loss: 2.3856
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2327 - loss: 2.2808 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2327 - loss: 2.2720
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2328 - loss: 2.2646
[1m 83/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2332 - loss: 2.2606
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2335 - loss: 2.2584
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2339 - loss: 2.2555
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2343 - loss: 2.2519 - val_accuracy: 0.3259 - val_loss: 1.6967
Epoch 4/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.1875
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2448 - loss: 2.1541 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2491 - loss: 2.1398
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2526 - loss: 2.1337
[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2545 - loss: 2.1294
[1m101/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2560 - loss: 2.1246
[1m121/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2573 - loss: 2.1203
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2583 - loss: 2.1166 - val_accuracy: 0.3650 - val_loss: 1.5656
Epoch 5/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.1016
[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2771 - loss: 2.0175 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2778 - loss: 2.0136
[1m 60/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2784 - loss: 2.0103
[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2805 - loss: 2.0034
[1m100/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2826 - loss: 1.9973
[1m120/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2841 - loss: 1.9934
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2851 - loss: 1.9901 - val_accuracy: 0.3796 - val_loss: 1.5413
Epoch 6/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 1.8686
[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 1.8918 
[1m 44/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3238 - loss: 1.8931
[1m 63/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3223 - loss: 1.8923
[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3220 - loss: 1.8887
[1m104/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3216 - loss: 1.8861
[1m125/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3208 - loss: 1.8839
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3203 - loss: 1.8830 - val_accuracy: 0.3909 - val_loss: 1.4839
Epoch 7/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3281 - loss: 1.6857
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3331 - loss: 1.7993 
[1m 40/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3335 - loss: 1.8014
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3322 - loss: 1.8069
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3322 - loss: 1.8092
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.8095
[1m128/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3324 - loss: 1.8084
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Epoch 8/92

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[1m 86/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3436 - loss: 1.7614
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Epoch 9/92

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[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3572 - loss: 1.7336
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3595 - loss: 1.7237
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Epoch 10/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3715 - loss: 1.6644 
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[1m 88/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3753 - loss: 1.6647
[1m109/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3763 - loss: 1.6625
[1m129/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3771 - loss: 1.6599
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3775 - loss: 1.6586 - val_accuracy: 0.4383 - val_loss: 1.3950
Epoch 11/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4097 - loss: 1.5515 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4085 - loss: 1.5672
[1m 61/137[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4078 - loss: 1.5697
[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4067 - loss: 1.5734
[1m103/137[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4053 - loss: 1.5758
[1m123/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4042 - loss: 1.5779
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4035 - loss: 1.5789 - val_accuracy: 0.4528 - val_loss: 1.3705
Epoch 12/92

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[1m 25/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4129 - loss: 1.5358 
[1m 48/137[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4152 - loss: 1.5406
[1m 71/137[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4140 - loss: 1.5457
[1m 94/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4134 - loss: 1.5472
[1m114/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4132 - loss: 1.5479
[1m136/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4130 - loss: 1.5483
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4129 - loss: 1.5483 - val_accuracy: 0.4445 - val_loss: 1.3655
Epoch 13/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4223 - loss: 1.5045 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4180 - loss: 1.5095
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4189 - loss: 1.5104
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[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4200 - loss: 1.5090
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Epoch 14/92

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Epoch 15/92

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Epoch 16/92

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[1m 97/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4394 - loss: 1.4352
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4404 - loss: 1.4346
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Epoch 17/92

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[1m 23/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4680 - loss: 1.4025 
[1m 43/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4585 - loss: 1.4136
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[1m 84/137[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4533 - loss: 1.4165
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[1m124/137[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step - accuracy: 0.4526 - loss: 1.4146
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4525 - loss: 1.4143 - val_accuracy: 0.4634 - val_loss: 1.3594
Epoch 18/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4399 - loss: 1.4180 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4461 - loss: 1.4073
[1m 62/137[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4502 - loss: 1.3968
[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4525 - loss: 1.3908
[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4543 - loss: 1.3880
[1m122/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4554 - loss: 1.3868
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4561 - loss: 1.3863 - val_accuracy: 0.4680 - val_loss: 1.3548
Epoch 19/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4771 - loss: 1.3512 
[1m 41/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4775 - loss: 1.3574
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[1m 82/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4773 - loss: 1.3610
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Epoch 20/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4818 - loss: 1.3252 
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Epoch 21/92

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 1s/step
[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 863us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 43.77 [%]
F1-score capturado en la ejecución 28: 41.39 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 55/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 938us/step
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[1m181/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 841us/step
[1m248/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 817us/step
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[1m377/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 804us/step
[1m444/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 797us/step
[1m509/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 794us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 886us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 54/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 946us/step
[1m114/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 887us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 46.86 [%]
Global F1 score (validation) = 44.43 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.5523350e-03 1.3507083e-03 8.2666543e-04 ... 3.9737855e-04
  4.9810368e-04 8.2324230e-04]
 [1.8205412e-03 2.7096937e-03 1.4919955e-03 ... 9.4781397e-04
  1.2355066e-03 2.0861186e-03]
 [9.4842562e-04 1.4781029e-03 7.3834939e-04 ... 6.1070849e-04
  7.8544882e-04 6.5748801e-04]
 ...
 [7.7209061e-06 7.7217301e-06 6.4516121e-06 ... 2.5220236e-03
  5.5047561e-04 9.6427795e-04]
 [7.9947640e-06 8.8354236e-06 7.4833602e-06 ... 1.7854552e-03
  5.5535085e-04 1.3311557e-03]
 [4.1670725e-04 3.0375557e-04 6.0586911e-04 ... 3.3860072e-01
  4.7186870e-04 1.3229990e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.18 [%]
Global accuracy score (test) = 43.14 [%]
Global F1 score (train) = 53.32 [%]
Global F1 score (test) = 40.44 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.20      0.21       161
 CAMINAR CON MÓVIL O LIBRO       0.20      0.23      0.21       161
       CAMINAR USUAL SPEED       0.24      0.34      0.28       161
            CAMINAR ZIGZAG       0.17      0.07      0.10       161
          DE PIE BARRIENDO       0.45      0.42      0.44       161
   DE PIE DOBLANDO TOALLAS       0.32      0.17      0.22       161
    DE PIE MOVIENDO LIBROS       0.31      0.46      0.37       161
          DE PIE USANDO PC       0.73      0.83      0.78       161
        FASE REPOSO CON K5       0.51      0.88      0.64       161
INCREMENTAL CICLOERGOMETRO       0.99      0.92      0.95       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.43      0.08      0.14       161
      SENTADO VIENDO LA TV       0.22      0.43      0.29       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.75      0.62       161
                    TROTAR       0.91      0.74      0.82       138

                  accuracy                           0.43      2392
                 macro avg       0.42      0.43      0.40      2392
              weighted avg       0.41      0.43      0.40      2392

2025-10-27 12:45:39.446622: 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`.
2025-10-27 12:45:39.459639: 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 00:00:1761565539.473956  757203 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761565539.478125  757203 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761565539.489220  757203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565539.489244  757203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565539.489247  757203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761565539.489250  757203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-27 12:45:39.492595: 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 00:00:1761565541.913933  757203 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12265 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/92
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761565544.498679  757341 service.cc:152] XLA service 0x79d734002b20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761565544.498746  757341 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-27 12:45:44.554215: 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 00:00:1761565544.850546  757341 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761565548.754791  757341 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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2025-10-27 12:45:53.437396: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_1', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 16 bytes spill stores, 16 bytes spill loads
ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_3', 16 bytes spill stores, 16 bytes spill loads


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
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Epoch 2/92

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Epoch 3/92

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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2227 - loss: 2.2545
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Epoch 4/92

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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2605 - loss: 2.0789 - val_accuracy: 0.3455 - val_loss: 1.5906
Epoch 5/92

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Epoch 6/92

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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.2959 - loss: 1.9201
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Epoch 7/92

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Epoch 8/92

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[1m 22/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3065 - loss: 1.8444 
[1m 42/137[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3129 - loss: 1.8277
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3210 - loss: 1.8076
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3267 - loss: 1.7881 - val_accuracy: 0.4057 - val_loss: 1.4361
Epoch 9/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3803 - loss: 1.6856 
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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3740 - loss: 1.6877
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[1m119/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3716 - loss: 1.6896
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3706 - loss: 1.6903 - val_accuracy: 0.4221 - val_loss: 1.4141
Epoch 10/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3698 - loss: 1.6545 
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[1m 81/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3775 - loss: 1.6529
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Epoch 11/92

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[1m 21/137[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3689 - loss: 1.6583 
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[1m102/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3814 - loss: 1.6210
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[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3837 - loss: 1.6163 - val_accuracy: 0.4344 - val_loss: 1.3850
Epoch 12/92

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Epoch 13/92

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Epoch 14/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4230 - loss: 1.4981 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4260 - loss: 1.5014
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[1m 95/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4267 - loss: 1.5025
[1m115/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4266 - loss: 1.5020
[1m135/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4266 - loss: 1.5014
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4266 - loss: 1.5012 - val_accuracy: 0.4595 - val_loss: 1.3382
Epoch 15/92

[1m  1/137[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4219 - loss: 1.4721
[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4269 - loss: 1.4738 
[1m 39/137[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4246 - loss: 1.4829
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[1m 77/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4283 - loss: 1.4719
[1m 95/137[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4297 - loss: 1.4693
[1m114/137[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4310 - loss: 1.4676
[1m133/137[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.4320 - loss: 1.4665
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.4322 - loss: 1.4662 - val_accuracy: 0.4599 - val_loss: 1.3525
Epoch 16/92

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[1m 20/137[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4458 - loss: 1.4749 
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[1m 79/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4485 - loss: 1.4380
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4483 - loss: 1.4359
[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4476 - loss: 1.4357
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Epoch 17/92

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[1m 80/137[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4469 - loss: 1.4233
[1m 99/137[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4476 - loss: 1.4229
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Epoch 18/92

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Epoch 19/92

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[1m118/137[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step - accuracy: 0.4681 - loss: 1.3707
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 1s/step
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 878us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 43.14 [%]
F1-score capturado en la ejecución 29: 40.44 [%]

=== 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, 6, 256)         │       192,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 256)         │       196,864 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 256)         │           512 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 256)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 256)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         3,855 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 788,751 (3.01 MB)
 Trainable params: 788,751 (3.01 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2392, 6, 250)
(17480, 6, 250)

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[1m 56/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 911us/step
[1m118/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 861us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 46.38 [%]
Global F1 score (validation) = 44.27 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.54657732e-03 1.49230962e-03 9.81579651e-04 ... 1.85611934e-04
  1.02388870e-03 3.61068523e-04]
 [2.41464307e-03 2.92744837e-03 1.98363862e-03 ... 7.06904742e-04
  2.27277866e-03 5.27796976e-04]
 [7.06372375e-04 9.77377524e-04 6.90404268e-04 ... 4.93750675e-04
  9.02847329e-04 1.97433692e-04]
 ...
 [1.24845365e-05 6.88989667e-06 6.85661780e-06 ... 1.18182902e-03
  5.92039316e-04 6.32155454e-04]
 [1.82898129e-05 9.29978069e-06 1.06497719e-05 ... 2.32644565e-03
  6.83522492e-04 1.19873998e-03]
 [5.38610446e-04 2.90752563e-04 7.69717037e-04 ... 2.79311717e-01
  6.76565629e-04 4.25736001e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 55.59 [%]
Global accuracy score (test) = 46.99 [%]
Global F1 score (train) = 53.82 [%]
Global F1 score (test) = 45.87 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.22      0.23       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.31      0.26       161
       CAMINAR USUAL SPEED       0.25      0.24      0.25       161
            CAMINAR ZIGZAG       0.11      0.07      0.08       161
          DE PIE BARRIENDO       0.45      0.48      0.47       161
   DE PIE DOBLANDO TOALLAS       0.34      0.22      0.27       161
    DE PIE MOVIENDO LIBROS       0.36      0.51      0.42       161
          DE PIE USANDO PC       0.77      0.81      0.79       161
        FASE REPOSO CON K5       0.51      0.87      0.64       161
INCREMENTAL CICLOERGOMETRO       0.99      0.90      0.94       161
           SENTADO LEYENDO       0.55      0.39      0.46       161
         SENTADO USANDO PC       0.46      0.56      0.51       161
      SENTADO VIENDO LA TV       0.41      0.12      0.18       161
   SUBIR Y BAJAR ESCALERAS       0.50      0.64      0.56       161
                    TROTAR       0.91      0.75      0.83       138

                  accuracy                           0.47      2392
                 macro avg       0.47      0.47      0.46      2392
              weighted avg       0.47      0.47      0.46      2392


Accuracy capturado en la ejecución 30: 46.99 [%]
F1-score capturado en la ejecución 30: 45.87 [%]

=== RESUMEN FINAL ===
Accuracies: [46.91, 46.2, 44.73, 46.74, 45.36, 45.99, 44.98, 47.83, 44.4, 44.9, 44.48, 42.35, 46.11, 44.86, 45.19, 47.07, 45.32, 44.02, 45.19, 46.4, 45.9, 44.82, 45.07, 46.53, 46.07, 48.24, 43.69, 43.77, 43.14, 46.99]
F1-scores: [44.76, 43.92, 43.86, 44.42, 44.06, 44.38, 43.13, 46.56, 42.08, 43.28, 43.35, 39.27, 44.27, 42.75, 43.33, 44.31, 43.46, 42.81, 42.72, 45.4, 44.81, 43.4, 42.57, 45.41, 45.2, 46.43, 41.69, 41.39, 40.44, 45.87]
Accuracy mean: 45.4417 | std: 1.3320
F1 mean: 43.6443 | std: 1.6293

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_ESANN_acc_gyr_17_classes/metrics_test.npz
