2025-11-08 17:32:40.503201: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:32:40.514908: 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 00:00:1762619560.528910 1265113 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762619560.533324 1265113 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762619560.543546 1265113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762619560.543564 1265113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762619560.543566 1265113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762619560.543568 1265113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:32:40.546795: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-08 17:32:43,607	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-08 17:32:44,336	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-08 17:32:44,399	INFO trial.py:182 -- Creating a new dirname dir_8d928_f19f because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,403	INFO trial.py:182 -- Creating a new dirname dir_8d928_d426 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,405	INFO trial.py:182 -- Creating a new dirname dir_8d928_d1cd because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,407	INFO trial.py:182 -- Creating a new dirname dir_8d928_1645 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,410	INFO trial.py:182 -- Creating a new dirname dir_8d928_afd7 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,412	INFO trial.py:182 -- Creating a new dirname dir_8d928_f749 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,415	INFO trial.py:182 -- Creating a new dirname dir_8d928_b12f because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,418	INFO trial.py:182 -- Creating a new dirname dir_8d928_0b51 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,420	INFO trial.py:182 -- Creating a new dirname dir_8d928_ce3f because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,424	INFO trial.py:182 -- Creating a new dirname dir_8d928_13d7 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,426	INFO trial.py:182 -- Creating a new dirname dir_8d928_3ffd because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,429	INFO trial.py:182 -- Creating a new dirname dir_8d928_2b21 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,432	INFO trial.py:182 -- Creating a new dirname dir_8d928_2898 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,435	INFO trial.py:182 -- Creating a new dirname dir_8d928_8b41 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,438	INFO trial.py:182 -- Creating a new dirname dir_8d928_3cb8 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,444	INFO trial.py:182 -- Creating a new dirname dir_8d928_d79d because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,448	INFO trial.py:182 -- Creating a new dirname dir_8d928_6738 because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,452	INFO trial.py:182 -- Creating a new dirname dir_8d928_6ffb because trial dirname 'dir_8d928' already exists.
2025-11-08 17:32:44,457	INFO trial.py:182 -- Creating a new dirname dir_8d928_14e6 because trial dirname 'dir_8d928' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     CAPTURE24_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator              │
│ Scheduler                        FIFOScheduler                      │
│ Number of trials                 20                                 │
╰─────────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_CAPTURE24_acc_17_classes/CAPTURE24_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-08_17-32-42_885993_1265113/artifacts/2025-11-08_17-32-44/CAPTURE24_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-08 17:32:44. Total running time: 0s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    PENDING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    PENDING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    PENDING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    PENDING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    PENDING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    PENDING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    PENDING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    PENDING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    PENDING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    PENDING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    PENDING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    PENDING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    PENDING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    PENDING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    PENDING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    PENDING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    PENDING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    PENDING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    PENDING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    PENDING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            23 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    96 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00383 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            23 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            22 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            23 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00024 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            24 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    96 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00401 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            25 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    96 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00016 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            23 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00059 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            29 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    96 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            28 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    96 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00063 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            15 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            25 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00056 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            27 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_8d928 config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                           15 │
│ funcion_activacion             relu │
│ num_resblocks                     1 │
│ numero_filtros                   32 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                 16 │
│ tasa_aprendizaje             0.0029 │
╰─────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            25 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00215 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje               0.001 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            29 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00018 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            19 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            24 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            20 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00134 │
╰──────────────────────────────────────╯
Trial trial_8d928 started with configuration:
[36m(train_cnn_ray_tune pid=1266719)[0m 2025-11-08 17:32:47.615805: 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=1266719)[0m 2025-11-08 17:32:47.637684: 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=1266719)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=1266719)[0m E0000 00:00:1762619567.665166 1267897 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=1266719)[0m E0000 00:00:1762619567.674938 1267897 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=1266719)[0m W0000 00:00:1762619567.695929 1267897 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=1266719)[0m W0000 00:00:1762619567.695981 1267897 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=1266719)[0m W0000 00:00:1762619567.695984 1267897 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=1266719)[0m W0000 00:00:1762619567.695986 1267897 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=1266719)[0m 2025-11-08 17:32:47.702454: 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=1266719)[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=1266719)[0m 2025-11-08 17:32:50.798573: 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=1266719)[0m 2025-11-08 17:32:50.798621: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=1266719)[0m 2025-11-08 17:32:50.798628: 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=1266719)[0m 2025-11-08 17:32:50.798632: 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=1266719)[0m 2025-11-08 17:32:50.798636: 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=1266719)[0m 2025-11-08 17:32:50.798639: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=1266719)[0m 2025-11-08 17:32:50.798836: 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=1266719)[0m 2025-11-08 17:32:50.798864: 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=1266719)[0m 2025-11-08 17:32:50.798867: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_8d928 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            16 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00472 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266719)[0m Epoch 1/23
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m Epoch 1/19[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=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 17:33:14. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    RUNNING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    RUNNING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    RUNNING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    RUNNING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m Epoch 2/29
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m Epoch 2/20
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:48[0m 199ms/step - accuracy: 0.3750 - loss: 1.6877
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m ��━[0m [1m25s[0m 62ms/step - accuracy: 0.0856 - loss: 3.2179
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m47s[0m 77ms/step - accuracy: 0.3006 - loss: 1.9569 - val_accuracy: 0.3338 - val_loss: 2.0781
[36m(train_cnn_ray_tune pid=1266719)[0m Epoch 2/23
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 166ms/step - accuracy: 0.4062 - loss: 1.4205
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 49/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 64ms/step - accuracy: 0.3627 - loss: 1.6327[32m [repeated 110x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
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[1m 972/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m5s[0m 47ms/step - accuracy: 0.1707 - loss: 2.6926[32m [repeated 60x across cluster][0m
[36m(train_cnn_ray_tune pid=1266736)[0m 
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[1m174/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m19s[0m 52ms/step - accuracy: 0.3432 - loss: 1.7857[32m [repeated 42x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:04[0m 118ms/step - accuracy: 0.3125 - loss: 1.9155
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m50s[0m 83ms/step - accuracy: 0.2445 - loss: 2.3037 - val_accuracy: 0.3462 - val_loss: 1.6027[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266722)[0m Epoch 2/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 17:33:44. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    RUNNING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    RUNNING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    RUNNING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    RUNNING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 815/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m16s[0m 61ms/step - accuracy: 0.0898 - loss: 3.1930[32m [repeated 217x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 561/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m42s[0m 80ms/step - accuracy: 0.1541 - loss: 2.8561
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m1053/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 48ms/step - accuracy: 0.1938 - loss: 2.3934[32m [repeated 125x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 150ms/step - accuracy: 0.3125 - loss: 2.0311
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[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m 36/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 75ms/step - accuracy: 0.2609 - loss: 2.0641
[1m 37/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 75ms/step - accuracy: 0.2611 - loss: 2.0638
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[1m519/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 89ms/step - accuracy: 0.1592 - loss: 2.7668[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m51s[0m 85ms/step - accuracy: 0.2231 - loss: 2.2231 - val_accuracy: 0.2597 - val_loss: 2.0982
[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 2/24
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m56s[0m 96ms/step - accuracy: 0.1718 - loss: 2.7531 - val_accuracy: 0.3316 - val_loss: 2.0103
[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 2/15
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:23[0m 153ms/step - accuracy: 0.0938 - loss: 2.3715
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m Epoch 2/22
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:54[0m 105ms/step - accuracy: 0.5625 - loss: 1.9753
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m Epoch 2/19
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:10[0m 120ms/step - accuracy: 0.1250 - loss: 2.5568
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 2/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m Epoch 3/29[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 2/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 17:34:14. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    RUNNING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    RUNNING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    RUNNING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    RUNNING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m Epoch 3/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m Epoch 3/28
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m90s[0m 79ms/step - accuracy: 0.2321 - loss: 2.2864 - val_accuracy: 0.3781 - val_loss: 1.6726
[36m(train_cnn_ray_tune pid=1266766)[0m Epoch 2/25
[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 3/24
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 134ms/step - accuracy: 0.4062 - loss: 2.0022[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m Epoch 2/23
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m   2/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 73ms/step - accuracy: 0.2031 - loss: 1.7692 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[1m 100/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 64ms/step - accuracy: 0.3630 - loss: 1.8438[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m Epoch 4/29[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m Epoch 2/25[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 3/27
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:24[0m 156ms/step - accuracy: 0.3750 - loss: 1.9101
Trial status: 20 RUNNING
Current time: 2025-11-08 17:34:44. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    RUNNING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    RUNNING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    RUNNING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    RUNNING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m Epoch 3/19[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m Epoch 4/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m Epoch 3/23[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 4/24
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 17:35:14. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    RUNNING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    RUNNING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    RUNNING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    RUNNING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m Epoch 5/29
[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:17[0m 142ms/step - accuracy: 0.5625 - loss: 1.3690
[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m Epoch 5/20
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 130ms/step - accuracy: 0.4062 - loss: 1.2331
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 3/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m Epoch 5/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 4/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m1018/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m5s[0m 74ms/step - accuracy: 0.3962 - loss: 1.6129
[1m1019/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m5s[0m 74ms/step - accuracy: 0.3962 - loss: 1.6128[32m [repeated 39x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-11-08 17:35:44. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    RUNNING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    RUNNING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    RUNNING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    RUNNING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m45s[0m 83ms/step - accuracy: 0.4177 - loss: 1.5347 - val_accuracy: 0.4239 - val_loss: 1.5062
[36m(train_cnn_ray_tune pid=1266722)[0m Epoch 4/23
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 949/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m10s[0m 75ms/step - accuracy: 0.3130 - loss: 1.9830[32m [repeated 157x across cluster][0m
[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:58[0m 218ms/step - accuracy: 0.4375 - loss: 1.5146
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m Epoch 3/25
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:08[0m 172ms/step - accuracy: 0.3125 - loss: 1.8253
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m   2/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16[0m 70ms/step - accuracy: 0.3594 - loss: 1.7309 
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m162/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m25s[0m 65ms/step - accuracy: 0.6025 - loss: 1.0450
[1m163/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m25s[0m 65ms/step - accuracy: 0.6025 - loss: 1.0451[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m519/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 97ms/step - accuracy: 0.3015 - loss: 2.0237
[1m520/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m2s[0m 97ms/step - accuracy: 0.3015 - loss: 2.0236[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m   2/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:20[0m 74ms/step - accuracy: 0.1406 - loss: 1.9668 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m   5/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m56s[0m 52ms/step - accuracy: 0.1831 - loss: 1.8963 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m Epoch 6/29[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 4/29[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m Epoch 3/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m Epoch 6/20
[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m389/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m9s[0m 63ms/step - accuracy: 0.4364 - loss: 1.4612 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m Epoch 3/25
[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 17:36:14. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    RUNNING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    RUNNING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    RUNNING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    RUNNING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m Epoch 3/24[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m Epoch 5/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 5/27
[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:48:29[0m 12s/step - accuracy: 0.6875 - loss: 1.0205
[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 6/24
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 4/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m Epoch 4/25[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 17:36:45. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.000626191         28 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 96                  3                 1          0.00382893          23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   16                 64                  5                 0          0.00056442          25 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22 │
│ trial_8d928    RUNNING            2   adam            relu                                   16                 32                  5                 1          0.00290016          15 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25 │
│ trial_8d928    RUNNING            3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24 │
│ trial_8d928    RUNNING            2   adam            relu                                   32                 32                  3                 0          0.000176167         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18 │
│ trial_8d928    RUNNING            2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 64                  5                 1          0.00133923          20 │
│ trial_8d928    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23 │
│ trial_8d928    RUNNING            2   adam            tanh                                   16                 96                  5                 0          0.000155258         25 │
│ trial_8d928    RUNNING            2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27 │
│ trial_8d928    RUNNING            2   adam            tanh                                   32                 96                  5                 0          0.00401198          24 │
│ trial_8d928    RUNNING            3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29 │
│ trial_8d928    RUNNING            3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m Epoch 7/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m 5/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step  
[1m 9/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m12/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[1m14/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:28[0m 163ms/step - accuracy: 0.5312 - loss: 1.0961[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step
[1m25/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m27/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 24ms/step
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 24ms/step
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 25ms/step
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m46/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 26ms/step
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 25ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 26ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 26ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 26ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 26ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 26ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 26ms/step
[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m490/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 59ms/step - accuracy: 0.4557 - loss: 1.4128
[1m491/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 59ms/step - accuracy: 0.4557 - loss: 1.4128
[1m492/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 59ms/step - accuracy: 0.4557 - loss: 1.4128
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 26ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 26ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m1052/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m2s[0m 61ms/step - accuracy: 0.4204 - loss: 1.6072
[1m1053/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m2s[0m 61ms/step - accuracy: 0.4204 - loss: 1.6072[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m 152/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m45s[0m 49ms/step - accuracy: 0.3833 - loss: 1.6038[32m [repeated 269x across cluster][0m
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m1071/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 61ms/step - accuracy: 0.4205 - loss: 1.6068[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 31ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 31ms/step
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m53s[0m 48ms/step - accuracy: 0.3831 - loss: 1.6211 - val_accuracy: 0.3925 - val_loss: 1.5532
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 311/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m27s[0m 36ms/step - accuracy: 0.3436 - loss: 1.7334
[1m 313/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m27s[0m 36ms/step - accuracy: 0.3437 - loss: 1.7333[32m [repeated 185x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m   3/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 43ms/step - accuracy: 0.4965 - loss: 1.3641  
[1m   5/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 36ms/step - accuracy: 0.4542 - loss: 1.3906
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 75ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m  4/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step 
[1m  7/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m 10/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m 39/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m Epoch 5/15
[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:14[0m 124ms/step - accuracy: 0.5625 - loss: 1.2585
[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m125/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m137/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266720)[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=1266720)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266765)[0m 2025-11-08 17:32:48.097597: 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=1266765)[0m 2025-11-08 17:32:48.117042: 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=1266767)[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=1266767)[0m E0000 00:00:1762619568.107316 1268030 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=1266767)[0m E0000 00:00:1762619568.115359 1268030 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=1266767)[0m W0000 00:00:1762619568.134759 1268030 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=1266767)[0m 2025-11-08 17:32:48.141008: 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=1266767)[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=1266762)[0m 2025-11-08 17:32:51.521881: 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=1266762)[0m 2025-11-08 17:32:51.521936: 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=1266762)[0m 2025-11-08 17:32:51.521943: 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=1266762)[0m 2025-11-08 17:32:51.521947: 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=1266762)[0m 2025-11-08 17:32:51.521951: 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=1266762)[0m 2025-11-08 17:32:51.521954: 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=1266762)[0m 2025-11-08 17:32:51.522200: 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=1266762)[0m 2025-11-08 17:32:51.522236: 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=1266762)[0m 2025-11-08 17:32:51.522239: 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=1266720)[0m 
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[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m147/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m150/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 22ms/step
[1m152/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266736)[0m Epoch 8/29
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m155/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 22ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266720)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:25[0m 156ms/step - accuracy: 0.4688 - loss: 1.3905

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:36:55. Total running time: 4min 11s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              248.58 │
│ time_total_s                  248.58 │
│ training_iteration                 1 │
│ val_accuracy                 0.41443 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:36:56. Total running time: 4min 11s
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:19[0m 146ms/step - accuracy: 0.3438 - loss: 1.9233
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m Epoch 6/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m Epoch 4/25
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m Epoch 8/20

Trial status: 1 TERMINATED | 19 RUNNING
Current time: 2025-11-08 17:37:15. Total running time: 4min 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              2   adam            relu                                   32                 96                  3                 1          0.00382893          23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   16                 32                  5                 1          0.00290016          15                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   32                 32                  3                 0          0.000176167         29                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 64                  5                 1          0.00133923          20                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1             248.58         0.414427 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m Epoch 4/18
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m Epoch 9/29
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 20ms/step
[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m Epoch 6/19[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:48[0m 154ms/step - accuracy: 0.4375 - loss: 1.6054[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 24ms/step
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m  9/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 12/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 15/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 18/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m   7/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:07[0m 62ms/step - accuracy: 0.4572 - loss: 1.4896
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 21/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 23/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 29/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 57/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 68/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 75/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 79/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 83/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 85/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 87/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 92/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m102/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m107/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266719)[0m 
[1m112/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m  19/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:01[0m 57ms/step - accuracy: 0.4486 - loss: 1.4953
[1m  21/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 56ms/step - accuracy: 0.4473 - loss: 1.4982 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m93s[0m 85ms/step - accuracy: 0.4183 - loss: 1.5578 - val_accuracy: 0.4042 - val_loss: 1.5747[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266719)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:37:36. Total running time: 4min 51s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              288.99 │
│ time_total_s                  288.99 │
│ training_iteration                 1 │
│ val_accuracy                 0.42885 │
╰──────────────────────────────────────╯

[36m(train_cnn_ray_tune pid=1266719)[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=1266719)[0m   _log_deprecation_warning(
Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:37:36. Total running time: 4min 51s
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[36m(train_cnn_ray_tune pid=1266747)[0m Epoch 4/25[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 7/15[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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Trial status: 2 TERMINATED | 18 RUNNING
Current time: 2025-11-08 17:37:45. Total running time: 5min 0s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   16                 32                  5                 1          0.00290016          15                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   32                 32                  3                 0          0.000176167         29                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 64                  5                 1          0.00133923          20                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1             248.58         0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1             288.99         0.428854 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 5/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m24/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m35/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m40/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m43/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 24ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 24ms/step
[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 24ms/step
[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:13[0m 177ms/step - accuracy: 0.3750 - loss: 1.6924
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step
[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 30ms/step
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 53/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 59/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 66/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 70/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 73/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 80/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 86/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 89/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 91/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 7/24[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m78s[0m 69ms/step - accuracy: 0.2680 - loss: 2.0028 - val_accuracy: 0.2571 - val_loss: 2.0170
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m 94/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m100/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m106/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m  5/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 53ms/step - accuracy: 0.2584 - loss: 2.0361
[1m  6/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 53ms/step - accuracy: 0.2562 - loss: 2.0416
[1m  7/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 53ms/step - accuracy: 0.2527 - loss: 2.0484
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m113/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m119/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m125/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m128/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 20ms/step
[1m133/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m135/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m144/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266767)[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=1266767)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m146/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 21ms/step
[1m148/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 42ms/step - accuracy: 0.5154 - loss: 1.2721[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m177/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m26s[0m 72ms/step - accuracy: 0.3614 - loss: 1.7181[32m [repeated 152x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m 18/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 64ms/step - accuracy: 0.2393 - loss: 2.0662
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m156/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266767)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 21ms/step

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:37:53. Total running time: 5min 9s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             306.236 │
│ time_total_s                 306.236 │
│ training_iteration                 1 │
│ val_accuracy                 0.39545 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:37:53. Total running time: 5min 9s
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 130ms/step - accuracy: 0.5938 - loss: 1.0846
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m Epoch 10/29
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m Epoch 8/23
[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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Trial status: 3 TERMINATED | 17 RUNNING
Current time: 2025-11-08 17:38:15. Total running time: 5min 30s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   16                 32                  5                 1          0.00290016          15                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   32                 32                  3                 0          0.000176167         29                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:57[0m 163ms/step - accuracy: 0.5625 - loss: 1.2428
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m Epoch 5/25
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m Epoch 7/16
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 428ms/step
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m25/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m 936/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m6s[0m 43ms/step - accuracy: 0.4047 - loss: 1.5251
[1m 937/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m6s[0m 43ms/step - accuracy: 0.4047 - loss: 1.5251
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m28/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m187/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m20s[0m 56ms/step - accuracy: 0.5330 - loss: 1.2079[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m188/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m20s[0m 56ms/step - accuracy: 0.5330 - loss: 1.2081
[1m190/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m19s[0m 56ms/step - accuracy: 0.5328 - loss: 1.2084[32m [repeated 23x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m47/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 16ms/step
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 16ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266753)[0m Epoch 7/22[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m  87/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 31ms/step - accuracy: 0.3765 - loss: 1.6709
[1m  89/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 31ms/step - accuracy: 0.3766 - loss: 1.6705
[1m  91/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 31ms/step - accuracy: 0.3767 - loss: 1.6701
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:06[0m 121ms/step - accuracy: 0.5312 - loss: 1.6794[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m532/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 61ms/step - accuracy: 0.2471 - loss: 2.0546
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 69ms/step - accuracy: 0.4766 - loss: 1.6878  
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 73ms/step
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[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=1266736)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
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[36m(train_cnn_ray_tune pid=1266736)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:38:27. Total running time: 5min 43s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             339.975 │
│ time_total_s                 339.975 │
│ training_iteration                 1 │
│ val_accuracy                 0.42549 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:38:27. Total running time: 5min 43s
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 7/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 8/24
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m Epoch 7/15
[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m Epoch 5/18
[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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Trial status: 4 TERMINATED | 16 RUNNING
Current time: 2025-11-08 17:38:45. Total running time: 6min 0s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   16                 32                  5                 1          0.00290016          15                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m65s[0m 60ms/step - accuracy: 0.2556 - loss: 2.1667 - val_accuracy: 0.3206 - val_loss: 1.8651
[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 6/23
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:17[0m 126ms/step - accuracy: 0.5625 - loss: 1.7139
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 625ms/step
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[36m(train_cnn_ray_tune pid=1266722)[0m 
[1m 5/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 28ms/step
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m Epoch 6/25[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[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=1266722)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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[36m(train_cnn_ray_tune pid=1266722)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:38:55. Total running time: 6min 10s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             367.875 │
│ time_total_s                 367.875 │
│ training_iteration                 1 │
│ val_accuracy                 0.42036 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:38:55. Total running time: 6min 10s
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m Epoch 5/25
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m Epoch 5/24
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 132ms/step - accuracy: 0.2812 - loss: 1.9490
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 9/24
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 9/15
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 8/27[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 8/29

Trial status: 5 TERMINATED | 15 RUNNING
Current time: 2025-11-08 17:39:15. Total running time: 6min 30s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   16                 32                  5                 1          0.00290016          15                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m Epoch 6/25
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m Epoch 8/15
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 988/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m5s[0m 48ms/step - accuracy: 0.2700 - loss: 2.1050
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[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m 858/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m11s[0m 49ms/step - accuracy: 0.4014 - loss: 1.5348
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[1m 860/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m11s[0m 49ms/step - accuracy: 0.4014 - loss: 1.5348[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 826/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.3677 - loss: 1.6430[32m [repeated 304x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 832/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 38ms/step - accuracy: 0.3677 - loss: 1.6430 
[1m 834/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 38ms/step - accuracy: 0.3677 - loss: 1.6430
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m 337/1093[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m32s[0m 43ms/step - accuracy: 0.4224 - loss: 1.4629
[1m 339/1093[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m32s[0m 43ms/step - accuracy: 0.4223 - loss: 1.4630[32m [repeated 177x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m36s[0m 65ms/step - accuracy: 0.2511 - loss: 2.0374 - val_accuracy: 0.3028 - val_loss: 1.9881
[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 10/24
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m382/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m12s[0m 76ms/step - accuracy: 0.4124 - loss: 1.6422
[1m383/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m12s[0m 76ms/step - accuracy: 0.4124 - loss: 1.6422[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:41[0m 185ms/step - accuracy: 0.2812 - loss: 1.9698
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 57ms/step - accuracy: 0.3125 - loss: 1.8949  
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36s[0m 66ms/step - accuracy: 0.3125 - loss: 1.8971
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[1m 906/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m7s[0m 38ms/step - accuracy: 0.3676 - loss: 1.6430[32m [repeated 75x across cluster][0m
[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m 977/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m5s[0m 49ms/step - accuracy: 0.4010 - loss: 1.5366
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 792/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m17s[0m 60ms/step - accuracy: 0.4826 - loss: 1.3792
[1m 793/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m17s[0m 60ms/step - accuracy: 0.4826 - loss: 1.3791
[1m 794/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m17s[0m 60ms/step - accuracy: 0.4826 - loss: 1.3791
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 716/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m22s[0m 61ms/step - accuracy: 0.3904 - loss: 1.6543[32m [repeated 179x across cluster][0m
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m 844/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 40ms/step - accuracy: 0.4001 - loss: 1.5920 
[1m 846/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 40ms/step - accuracy: 0.4001 - loss: 1.5920
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m 54/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 55ms/step - accuracy: 0.2604 - loss: 1.9699
[1m 55/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 55ms/step - accuracy: 0.2603 - loss: 1.9700
[1m 56/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 55ms/step - accuracy: 0.2603 - loss: 1.9701
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m 717/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m23s[0m 63ms/step - accuracy: 0.4505 - loss: 1.4416
[1m 718/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m23s[0m 63ms/step - accuracy: 0.4506 - loss: 1.4415[32m [repeated 131x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m441/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m7s[0m 75ms/step - accuracy: 0.4128 - loss: 1.6428
[1m442/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m7s[0m 75ms/step - accuracy: 0.4128 - loss: 1.6428[32m [repeated 38x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m 87/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m24s[0m 54ms/step - accuracy: 0.2572 - loss: 1.9786[32m [repeated 31x across cluster][0m
Trial status: 5 TERMINATED | 15 RUNNING
Current time: 2025-11-08 17:39:45. Total running time: 7min 0s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   16                 32                  5                 1          0.00290016          15                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m451/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m7s[0m 75ms/step - accuracy: 0.4129 - loss: 1.6429[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[1m 89/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m24s[0m 54ms/step - accuracy: 0.2569 - loss: 1.9794[32m [repeated 30x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m41s[0m 75ms/step - accuracy: 0.4022 - loss: 1.6320 - val_accuracy: 0.4196 - val_loss: 1.5386
[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 10/15
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m 962/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m5s[0m 40ms/step - accuracy: 0.3997 - loss: 1.5930[32m [repeated 139x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 134ms/step - accuracy: 0.3125 - loss: 1.6086
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  2/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 50ms/step - accuracy: 0.3594 - loss: 1.5487  
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[1m1082/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 49ms/step - accuracy: 0.4006 - loss: 1.5380[32m [repeated 102x across cluster][0m
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 925/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m9s[0m 59ms/step - accuracy: 0.4822 - loss: 1.3782 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 806/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m17s[0m 60ms/step - accuracy: 0.3908 - loss: 1.6533[32m [repeated 162x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m60s[0m 55ms/step - accuracy: 0.2704 - loss: 2.1032 - val_accuracy: 0.3474 - val_loss: 1.8028
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   3/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 28ms/step - accuracy: 0.1944 - loss: 2.0012  
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m516/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 74ms/step - accuracy: 0.4131 - loss: 1.6439
[1m517/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 74ms/step - accuracy: 0.4131 - loss: 1.6439[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m527/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 74ms/step - accuracy: 0.4131 - loss: 1.6440[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 995/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m5s[0m 59ms/step - accuracy: 0.4821 - loss: 1.3774
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 9/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:25[0m 157ms/step - accuracy: 0.3750 - loss: 1.7933[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m Epoch 6/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 9/29[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m Epoch 6/23
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m32s[0m 58ms/step - accuracy: 0.2575 - loss: 2.0075 - val_accuracy: 0.3018 - val_loss: 1.8632
[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 11/24
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:07[0m 123ms/step - accuracy: 0.1875 - loss: 2.1013
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m49s[0m 44ms/step - accuracy: 0.4161 - loss: 1.4695 - val_accuracy: 0.3917 - val_loss: 1.6175
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 479/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m21s[0m 35ms/step - accuracy: 0.4172 - loss: 1.5437
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[1m 483/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m21s[0m 35ms/step - accuracy: 0.4173 - loss: 1.5437
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 563ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m380/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m9s[0m 60ms/step - accuracy: 0.4125 - loss: 1.6749 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step  
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m13/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m17/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m20/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m24/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 159/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 51ms/step - accuracy: 0.4740 - loss: 1.3514[32m [repeated 154x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m   3/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m55s[0m 51ms/step - accuracy: 0.3299 - loss: 1.9375  
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m27/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m31/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m35/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m38/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 548/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m24s[0m 45ms/step - accuracy: 0.2706 - loss: 2.0679
[1m 550/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m24s[0m 45ms/step - accuracy: 0.2706 - loss: 2.0678[32m [repeated 146x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m40/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m55/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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Trial status: 5 TERMINATED | 15 RUNNING
Current time: 2025-11-08 17:40:15. Total running time: 7min 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              2   adam            relu                                   16                 32                  5                 1          0.00290016          15                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m 88/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266747)[0m Epoch 6/25[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m 94/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m 91/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m24s[0m 54ms/step - accuracy: 0.2589 - loss: 1.9991
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:51[0m 157ms/step - accuracy: 0.1875 - loss: 2.3225
[36m(train_cnn_ray_tune pid=1266745)[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=1266745)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m76s[0m 70ms/step - accuracy: 0.4538 - loss: 1.4326 - val_accuracy: 0.4152 - val_loss: 1.6255[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m109/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 20ms/step
[1m112/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m120/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 20ms/step
[1m124/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m127/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m134/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[1m 588/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m18s[0m 36ms/step - accuracy: 0.4103 - loss: 1.5713
[1m 589/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m18s[0m 36ms/step - accuracy: 0.4103 - loss: 1.5713[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266745)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m  81/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 59ms/step - accuracy: 0.3896 - loss: 1.5694 
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m148/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1266745)[0m 
[1m156/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 20ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 20ms/step

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:40:18. Total running time: 7min 34s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             451.448 │
│ time_total_s                 451.448 │
│ training_iteration                 1 │
│ val_accuracy                  0.3917 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:40:18. Total running time: 7min 34s
[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 11/15
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:27[0m 136ms/step - accuracy: 0.5625 - loss: 1.2306
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m   5/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 53ms/step - accuracy: 0.5412 - loss: 1.2127 
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[36m(train_cnn_ray_tune pid=1266766)[0m Epoch 7/25
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 10/27
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m 28/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m34s[0m 67ms/step - accuracy: 0.4016 - loss: 1.6610
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 920/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m7s[0m 46ms/step - accuracy: 0.2755 - loss: 2.0505[32m [repeated 65x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:24[0m 156ms/step - accuracy: 0.3438 - loss: 1.5417[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 10/29
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:26[0m 158ms/step - accuracy: 0.2812 - loss: 1.6711
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:39[0m 146ms/step - accuracy: 0.3750 - loss: 1.7052
[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m Epoch 10/22[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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Trial status: 6 TERMINATED | 14 RUNNING
Current time: 2025-11-08 17:40:45. Total running time: 8min 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:36[0m 177ms/step - accuracy: 0.1562 - loss: 2.4605
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:11[0m 121ms/step - accuracy: 0.4375 - loss: 1.5834
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:21[0m 130ms/step - accuracy: 0.2500 - loss: 2.4350
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 8/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:57[0m 162ms/step - accuracy: 0.5000 - loss: 1.2619
[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m Epoch 8/25
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[1m1077/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 56ms/step - accuracy: 0.5316 - loss: 1.2144
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 56ms/step - accuracy: 0.5317 - loss: 1.2144[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m1088/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 56ms/step - accuracy: 0.5317 - loss: 1.2144
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[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m  29/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 51ms/step - accuracy: 0.4347 - loss: 1.4765
[1m  30/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 51ms/step - accuracy: 0.4344 - loss: 1.4781
[1m  31/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 51ms/step - accuracy: 0.4341 - loss: 1.4796[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m 137/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 49ms/step - accuracy: 0.4191 - loss: 1.5119[32m [repeated 247x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 812/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.3658 - loss: 1.6524
[1m 814/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.3658 - loss: 1.6524[32m [repeated 175x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m489/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 65ms/step - accuracy: 0.4098 - loss: 1.6457[32m [repeated 22x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m283/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m14s[0m 55ms/step - accuracy: 0.2368 - loss: 2.0691
[1m284/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m14s[0m 55ms/step - accuracy: 0.2368 - loss: 2.0690[32m [repeated 44x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m39s[0m 71ms/step - accuracy: 0.4089 - loss: 1.5797 - val_accuracy: 0.4091 - val_loss: 1.5212
[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 12/15
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m Epoch 7/18
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 11/27
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 749/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m12s[0m 37ms/step - accuracy: 0.4311 - loss: 1.5006
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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Trial status: 6 TERMINATED | 14 RUNNING
Current time: 2025-11-08 17:41:15. Total running time: 8min 31s
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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   16                 96                  5                 0          0.000155258         25                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m Epoch 10/16
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m Epoch 13/24[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m Epoch 7/24[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m   3/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 44ms/step - accuracy: 0.4340 - loss: 1.3654  
[36m(train_cnn_ray_tune pid=1266747)[0m Epoch 7/25[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1011/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m3s[0m 44ms/step - accuracy: 0.2970 - loss: 2.0031[32m [repeated 29x across cluster][0m
[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m 933/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m6s[0m 43ms/step - accuracy: 0.4117 - loss: 1.5223
[1m 935/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m6s[0m 43ms/step - accuracy: 0.4117 - loss: 1.5223[32m [repeated 39x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 216/1093[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m36s[0m 42ms/step - accuracy: 0.4106 - loss: 1.5676[32m [repeated 193x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 478/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m18s[0m 29ms/step - accuracy: 0.3832 - loss: 1.5837
[1m 480/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m17s[0m 29ms/step - accuracy: 0.3832 - loss: 1.5837
[1m 482/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m17s[0m 29ms/step - accuracy: 0.3832 - loss: 1.5838
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34s[0m 465ms/step
[1m 4/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step  
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m 7/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m33s[0m 61ms/step - accuracy: 0.4284 - loss: 1.5310 - val_accuracy: 0.4107 - val_loss: 1.5250
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 300/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m21s[0m 28ms/step - accuracy: 0.4337 - loss: 1.4723
[1m 302/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m21s[0m 28ms/step - accuracy: 0.4338 - loss: 1.4723[32m [repeated 144x across cluster][0m
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m13/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[1m17/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m25/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
[1m28/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 22ms/step
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m37/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 21ms/step
[1m42/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m413/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m5s[0m 39ms/step - accuracy: 0.2600 - loss: 2.0068
[1m415/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m5s[0m 39ms/step - accuracy: 0.2601 - loss: 2.0068[32m [repeated 36x across cluster][0m
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m67s[0m 61ms/step - accuracy: 0.5465 - loss: 1.2208 - val_accuracy: 0.4036 - val_loss: 1.7134[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 18/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 49ms/step - accuracy: 0.4389 - loss: 1.6248[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m55/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m511/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - accuracy: 0.4260 - loss: 1.6022[32m [repeated 60x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:07[0m 124ms/step - accuracy: 0.3438 - loss: 2.1527
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m58/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 22ms/step
[1m61/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 21ms/step
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 13/15
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 21ms/step
[1m72/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m74/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 24/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 49ms/step - accuracy: 0.4390 - loss: 1.6041
[1m 26/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 49ms/step - accuracy: 0.4381 - loss: 1.5997[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[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=1266766)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m147/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m151/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m155/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m158/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 468/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m17s[0m 29ms/step - accuracy: 0.4373 - loss: 1.4700
[1m 470/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m17s[0m 29ms/step - accuracy: 0.4373 - loss: 1.4700[32m [repeated 152x across cluster][0m
[36m(train_cnn_ray_tune pid=1266766)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 18ms/step

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:41:39. Total running time: 8min 55s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             532.574 │
│ time_total_s                 532.574 │
│ training_iteration                 1 │
│ val_accuracy                 0.40356 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:41:39. Total running time: 8min 55s
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m53s[0m 49ms/step - accuracy: 0.2973 - loss: 2.0016 - val_accuracy: 0.3575 - val_loss: 1.7320
[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 9/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:41[0m 93ms/step - accuracy: 0.4375 - loss: 1.8486
[1m   3/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34s[0m 32ms/step - accuracy: 0.4062 - loss: 1.7876 
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m139/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m18s[0m 46ms/step - accuracy: 0.4461 - loss: 1.5370
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:02[0m 114ms/step - accuracy: 0.5312 - loss: 1.5097
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 46ms/step - accuracy: 0.4757 - loss: 1.5428  
[1m  4/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 48ms/step - accuracy: 0.4681 - loss: 1.5480
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 884/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.3804 - loss: 1.5955[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m161/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m17s[0m 46ms/step - accuracy: 0.4459 - loss: 1.5346
[1m162/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m17s[0m 46ms/step - accuracy: 0.4459 - loss: 1.5346
[1m163/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m17s[0m 46ms/step - accuracy: 0.4459 - loss: 1.5345
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 904/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.3803 - loss: 1.5959
[1m 907/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.3803 - loss: 1.5960[32m [repeated 32x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 563/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m14s[0m 28ms/step - accuracy: 0.4380 - loss: 1.4703
[1m 565/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m14s[0m 28ms/step - accuracy: 0.4380 - loss: 1.4703
[1m 567/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m14s[0m 28ms/step - accuracy: 0.4380 - loss: 1.4703
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 913/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.3802 - loss: 1.5961
[1m 915/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.3802 - loss: 1.5962
[1m 917/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.3802 - loss: 1.5962
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 620/1093[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m20s[0m 44ms/step - accuracy: 0.5226 - loss: 1.2465[32m [repeated 175x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 643/1093[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m12s[0m 28ms/step - accuracy: 0.4381 - loss: 1.4712
[1m 645/1093[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m12s[0m 28ms/step - accuracy: 0.4381 - loss: 1.4713[32m [repeated 142x across cluster][0m
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 523ms/step
[36m(train_cnn_ray_tune pid=1266754)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 680ms/step
[1m 4/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step  
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m 3/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step  
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266721)[0m 
[1m 9/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step

Trial status: 7 TERMINATED | 13 RUNNING
Current time: 2025-11-08 17:41:45. Total running time: 9min 1s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              2   adam            tanh                                   32                 96                  5                 0          0.00401198          24                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   16                 96                  5                 0          0.000155258         25        1            532.574         0.403557 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[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=1266721)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 12/27
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266721)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:41:50. Total running time: 9min 5s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             542.744 │
│ time_total_s                 542.744 │
│ training_iteration                 1 │
│ val_accuracy                 0.26285 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:41:50. Total running time: 9min 5s
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:41:50. Total running time: 9min 6s
[36m(train_cnn_ray_tune pid=1266754)[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=1266754)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             542.944 │
│ time_total_s                 542.944 │
│ training_iteration                 1 │
│ val_accuracy                 0.38281 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:41:50. Total running time: 9min 6s
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266754)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m Epoch 11/16
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 167/1093[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m23s[0m 25ms/step - accuracy: 0.3681 - loss: 1.5999
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m55s[0m 50ms/step - accuracy: 0.5705 - loss: 1.1335 - val_accuracy: 0.4409 - val_loss: 1.6700
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 851/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 41ms/step - accuracy: 0.4243 - loss: 1.5403 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m Epoch 12/19
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:28[0m 81ms/step - accuracy: 0.5625 - loss: 1.4660
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m   4/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 19ms/step - accuracy: 0.5586 - loss: 1.3630 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 570ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 5/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step  
[1m 8/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m11/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m27s[0m 48ms/step - accuracy: 0.4421 - loss: 1.5229 - val_accuracy: 0.4208 - val_loss: 1.5170
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  3/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 36ms/step - accuracy: 0.4028 - loss: 1.6312
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 54/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 59/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 62/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 65/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
[1m 68/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 71/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
[1m 74/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m538/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - accuracy: 0.4401 - loss: 1.5689[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 77/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 87/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 90/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m34s[0m 31ms/step - accuracy: 0.4289 - loss: 1.5180 - val_accuracy: 0.4008 - val_loss: 1.4953[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 93/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m 99/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m105/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m111/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m117/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m545/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - accuracy: 0.4401 - loss: 1.5689
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 42ms/step - accuracy: 0.4401 - loss: 1.5689[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 14/15[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:54:50[0m 39s/step - accuracy: 0.3438 - loss: 1.6926[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m120/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m123/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m   4/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 20ms/step - accuracy: 0.4714 - loss: 1.3064 
[1m   7/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 19ms/step - accuracy: 0.4813 - loss: 1.3139
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m126/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 21ms/step
[1m129/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m132/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 21ms/step
[1m135/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m138/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
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[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m145/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266764)[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=1266764)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m148/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m151/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m154/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step
[1m157/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266764)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 21ms/step

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:42:06. Total running time: 9min 22s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             559.126 │
│ time_total_s                 559.126 │
│ training_iteration                 1 │
│ val_accuracy                 0.44091 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:42:06. Total running time: 9min 22s
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 251/1093[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m21s[0m 26ms/step - accuracy: 0.4364 - loss: 1.4814
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m 323/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m20s[0m 26ms/step - accuracy: 0.4420 - loss: 1.4765[32m [repeated 62x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 343/1093[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m19s[0m 26ms/step - accuracy: 0.4409 - loss: 1.4741
[1m 345/1093[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m19s[0m 26ms/step - accuracy: 0.4410 - loss: 1.4740[32m [repeated 105x across cluster][0m
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 699ms/step
[1m 3/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 30ms/step  
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 703/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 25ms/step - accuracy: 0.3847 - loss: 1.5931
[1m 705/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 25ms/step - accuracy: 0.3847 - loss: 1.5931
[1m 707/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 25ms/step - accuracy: 0.3848 - loss: 1.5931
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 26ms/step
[1m 8/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m52s[0m 47ms/step - accuracy: 0.5250 - loss: 1.2460 - val_accuracy: 0.3958 - val_loss: 1.7646
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m11/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[1m14/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m17/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m20/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 22ms/step
[1m23/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m27/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[1m30/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 21ms/step
[1m36/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 13/27
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:05[0m 120ms/step - accuracy: 0.5938 - loss: 1.2167
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 20ms/step
[1m42/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 20ms/step
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m193/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m16s[0m 46ms/step - accuracy: 0.4616 - loss: 1.5313[32m [repeated 91x across cluster][0m
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 20ms/step
[1m57/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m64/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m67/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 20ms/step
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m243/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m12s[0m 42ms/step - accuracy: 0.4416 - loss: 1.5133
[1m245/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m12s[0m 42ms/step - accuracy: 0.4416 - loss: 1.5132[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 28ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 29ms/step
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m1017/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m3s[0m 41ms/step - accuracy: 0.4909 - loss: 1.3107[32m [repeated 93x across cluster][0m
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 71ms/step
[1m  4/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step 
[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m  7/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[1m 11/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 25/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 31/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 305/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m20s[0m 26ms/step - accuracy: 0.4393 - loss: 1.4769
[1m 307/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m20s[0m 26ms/step - accuracy: 0.4394 - loss: 1.4767
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 45/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 57/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 64/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 70/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 85/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 91/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 19ms/step
[1m100/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m310/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 42ms/step - accuracy: 0.4423 - loss: 1.5109
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 19ms/step
[1m107/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m110/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 19ms/step
[1m113/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m316/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 42ms/step - accuracy: 0.4423 - loss: 1.5108
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m116/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 19ms/step
[1m119/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m122/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 19ms/step
[1m125/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
[1m128/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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Trial status: 10 TERMINATED | 10 RUNNING
Current time: 2025-11-08 17:42:15. Total running time: 9min 31s
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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23                                              │
│ trial_8d928    RUNNING              2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25        1            542.944         0.382806 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18        1            559.126         0.440909 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   16                 96                  5                 0          0.000155258         25        1            532.574         0.403557 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 96                  5                 0          0.00401198          24        1            542.744         0.262846 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266728)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:42:15. Total running time: 9min 31s
[36m(train_cnn_ray_tune pid=1266728)[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=1266728)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             568.186 │
│ time_total_s                 568.186 │
│ training_iteration                 1 │
│ val_accuracy                 0.39585 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:42:15. Total running time: 9min 31s
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m Epoch 8/24
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m51s[0m 46ms/step - accuracy: 0.4910 - loss: 1.3102 - val_accuracy: 0.3666 - val_loss: 1.8944
[36m(train_cnn_ray_tune pid=1266747)[0m Epoch 8/25
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 10/23
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:03[0m 113ms/step - accuracy: 0.5625 - loss: 0.8700
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[1m   5/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m45s[0m 42ms/step - accuracy: 0.5665 - loss: 1.1057
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:00[0m 110ms/step - accuracy: 0.5000 - loss: 1.6937
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m 859/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m5s[0m 25ms/step - accuracy: 0.4396 - loss: 1.4830[32m [repeated 39x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 885/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 25ms/step - accuracy: 0.4446 - loss: 1.4711
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 524ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 6/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step  
[1m11/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m16/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m519/547[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 43ms/step - accuracy: 0.4609 - loss: 1.5310[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m27/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m32/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m36/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m42/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m46/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m55/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m527/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 43ms/step - accuracy: 0.4609 - loss: 1.5309
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[36m(train_cnn_ray_tune pid=1266765)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m70/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 50ms/step
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 13/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m58s[0m 108ms/step - accuracy: 0.5000 - loss: 1.5760
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 26/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 36/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step
[1m 42/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 47/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 63/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 73/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m 78/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m31s[0m 28ms/step - accuracy: 0.3846 - loss: 1.5949 - val_accuracy: 0.3605 - val_loss: 1.8027
[36m(train_cnn_ray_tune pid=1266760)[0m Epoch 15/15
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 84/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m 89/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 93/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m 98/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 10ms/step
[1m104/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266765)[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=1266765)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m108/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m119/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 10ms/step
[1m124/159[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 25/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 34ms/step - accuracy: 0.4636 - loss: 1.5096
[1m 27/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 33ms/step - accuracy: 0.4634 - loss: 1.5097[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m129/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 10ms/step
[1m134/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m139/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 11ms/step
[1m145/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m151/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 11ms/step
[1m156/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1266765)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:42:27. Total running time: 9min 43s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             579.778 │
│ time_total_s                 579.778 │
│ training_iteration                 1 │
│ val_accuracy                 0.36047 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:42:27. Total running time: 9min 43s
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m1051/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 24ms/step - accuracy: 0.4399 - loss: 1.4821[32m [repeated 26x across cluster][0m
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m1088/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 24ms/step - accuracy: 0.4399 - loss: 1.4819
[1m1091/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 24ms/step - accuracy: 0.4399 - loss: 1.4819[32m [repeated 62x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 12/29
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 14/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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Trial status: 12 TERMINATED | 8 RUNNING
Current time: 2025-11-08 17:42:45. Total running time: 10min 1s
Logical resource usage: 8.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25        1            542.944         0.382806 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18        1            559.126         0.440909 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23        1            568.186         0.39585  │
│ trial_8d928    TERMINATED           2   adam            tanh                                   16                 96                  5                 0          0.000155258         25        1            532.574         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16        1            579.778         0.360474 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 96                  5                 0          0.00401198          24        1            542.744         0.262846 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m26/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m447/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m3s[0m 40ms/step - accuracy: 0.4611 - loss: 1.5119
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m29/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m33/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m39/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m46/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m536/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 41ms/step - accuracy: 0.4650 - loss: 1.5053[32m [repeated 60x across cluster][0m
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m63/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  4/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m  9/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 13/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 17/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 25/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 33/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 42/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 60/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 69/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 78/159[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 87/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m110/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m116/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 13/29
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m130/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 12ms/step
[1m135/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 76ms/step - accuracy: 0.4062 - loss: 1.7324
[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m140/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step
[1m145/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  4/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 25ms/step - accuracy: 0.4447 - loss: 1.6307
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[36m(train_cnn_ray_tune pid=1266760)[0m 
[1m150/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
[1m155/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  8/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 26ms/step - accuracy: 0.4372 - loss: 1.5905
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[36m(train_cnn_ray_tune pid=1266760)[0m 
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[36m(train_cnn_ray_tune pid=1266760)[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=1266760)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:42:54. Total running time: 10min 9s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             606.726 │
│ time_total_s                 606.726 │
│ training_iteration                 1 │
│ val_accuracy                 0.43083 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:42:54. Total running time: 10min 9s
[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 486ms/step
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 7/75[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step   
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m20/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m26/75[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[1m32/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1266753)[0m Epoch 14/22[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m40/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m53/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 8ms/step
[1m59/75[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m184/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 27ms/step - accuracy: 0.4576 - loss: 1.5101 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[1m125/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m11s[0m 28ms/step - accuracy: 0.4822 - loss: 1.4958[32m [repeated 49x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m65/75[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step
[1m71/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m1089/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 33ms/step - accuracy: 0.5147 - loss: 1.2453
[1m1092/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 33ms/step - accuracy: 0.5147 - loss: 1.2453[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m1083/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 33ms/step - accuracy: 0.5147 - loss: 1.2453[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m131/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m11s[0m 28ms/step - accuracy: 0.4814 - loss: 1.4962[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 62ms/step
[1m  9/159[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step 
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 16/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step
[1m 23/159[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 29/159[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step
[1m 36/159[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 42/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 56/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 70/159[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 83/159[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 130/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 24ms/step - accuracy: 0.4315 - loss: 1.4741
[1m 132/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 24ms/step - accuracy: 0.4318 - loss: 1.4736[32m [repeated 67x across cluster][0m
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:28[0m 81ms/step - accuracy: 0.3125 - loss: 2.0855
[1m   5/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 15ms/step - accuracy: 0.3883 - loss: 1.8153 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m 88/159[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 8ms/step
[1m107/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
[1m113/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266762)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:43:01. Total running time: 10min 16s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             613.609 │
│ time_total_s                 613.609 │
│ training_iteration                 1 │
│ val_accuracy                 0.42194 │
╰──────────────────────────────────────╯

[36m(train_cnn_ray_tune pid=1266762)[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=1266762)[0m   _log_deprecation_warning(
Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:43:01. Total running time: 10min 16s
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m Epoch 9/25
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m 802/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.4520 - loss: 1.4529[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 758/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3231 - loss: 1.8683
[1m 762/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3231 - loss: 1.8683[32m [repeated 27x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 14/29
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 78ms/step - accuracy: 0.3750 - loss: 1.5312
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m 386/1093[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m19s[0m 27ms/step - accuracy: 0.5340 - loss: 1.2076[32m [repeated 48x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m 43/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 27ms/step - accuracy: 0.4832 - loss: 1.4378
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 95ms/step - accuracy: 0.5625 - loss: 1.1812
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m1003/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 16ms/step - accuracy: 0.4527 - loss: 1.4507
[1m1006/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 16ms/step - accuracy: 0.4527 - loss: 1.4506
[1m1008/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 16ms/step - accuracy: 0.4527 - loss: 1.4506

Trial status: 14 TERMINATED | 6 RUNNING
Current time: 2025-11-08 17:43:15. Total running time: 10min 31s
Logical resource usage: 6.0/20 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            tanh                                   16                 64                  5                 0          0.00056442          25                                              │
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25        1            542.944         0.382806 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15        1            606.726         0.43083  │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18        1            559.126         0.440909 │
│ trial_8d928    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19        1            613.609         0.421937 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23        1            568.186         0.39585  │
│ trial_8d928    TERMINATED           2   adam            tanh                                   16                 96                  5                 0          0.000155258         25        1            532.574         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16        1            579.778         0.360474 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 96                  5                 0          0.00401198          24        1            542.744         0.262846 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m 508/1093[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 27ms/step - accuracy: 0.5328 - loss: 1.2081
[1m 510/1093[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 27ms/step - accuracy: 0.5327 - loss: 1.2081[32m [repeated 56x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 968/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 21ms/step - accuracy: 0.3238 - loss: 1.8694[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 787/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.4486 - loss: 1.4593
[1m 789/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.4486 - loss: 1.4593[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 34ms/step - accuracy: 0.4696 - loss: 1.4962 - val_accuracy: 0.3842 - val_loss: 1.6141
[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 16/27
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 688/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m10s[0m 25ms/step - accuracy: 0.4477 - loss: 1.4596[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m230/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.4925 - loss: 1.4297 
[1m232/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.4925 - loss: 1.4297
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m22s[0m 20ms/step - accuracy: 0.4529 - loss: 1.4497 - val_accuracy: 0.4259 - val_loss: 1.5364
[36m(train_cnn_ray_tune pid=1266753)[0m Epoch 15/22
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:39[0m 91ms/step - accuracy: 0.5000 - loss: 1.2059
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m   5/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 19ms/step - accuracy: 0.4098 - loss: 1.5191 
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m246/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.4925 - loss: 1.4298
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m248/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.4925 - loss: 1.4299
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m  34/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 18ms/step - accuracy: 0.4709 - loss: 1.4179
[1m  37/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 18ms/step - accuracy: 0.4717 - loss: 1.4149[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 12/23
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m33s[0m 30ms/step - accuracy: 0.4509 - loss: 1.4589 - val_accuracy: 0.4229 - val_loss: 1.5238
[36m(train_cnn_ray_tune pid=1266763)[0m Epoch 10/24
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:31[0m 84ms/step - accuracy: 0.2500 - loss: 1.6068
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 61ms/step - accuracy: 0.5000 - loss: 1.6307
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m545/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 30ms/step - accuracy: 0.4762 - loss: 1.4650[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m33s[0m 30ms/step - accuracy: 0.5320 - loss: 1.2038 - val_accuracy: 0.4036 - val_loss: 1.8616
[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 17/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m10/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m14/75[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m161/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m9s[0m 25ms/step - accuracy: 0.4892 - loss: 1.4195
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m19/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m28/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m32/75[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m41/75[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 33ms/step - accuracy: 0.4762 - loss: 1.4650 - val_accuracy: 0.3830 - val_loss: 1.6247
[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m168/547[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 26ms/step - accuracy: 0.4893 - loss: 1.4456
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m60/75[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m68/75[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 75ms/step
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[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=1266747)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266747)[0m 
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[36m(train_cnn_ray_tune pid=1266747)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 12ms/step

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:43:39. Total running time: 10min 55s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             652.044 │
│ time_total_s                 652.044 │
│ training_iteration                 1 │
│ val_accuracy                 0.40356 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:43:39. Total running time: 10min 55s
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 18ms/step - accuracy: 0.4662 - loss: 1.4135 - val_accuracy: 0.4190 - val_loss: 1.5266
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m Epoch 16/22
[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[1m1015/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 19ms/step - accuracy: 0.3366 - loss: 1.8467[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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Trial status: 15 TERMINATED | 5 RUNNING
Current time: 2025-11-08 17:43:45. Total running time: 11min 1s
Logical resource usage: 5.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22                                              │
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           3   adam            tanh                                   16                 64                  5                 0          0.00056442          25        1            652.044         0.403557 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25        1            542.944         0.382806 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15        1            606.726         0.43083  │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18        1            559.126         0.440909 │
│ trial_8d928    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19        1            613.609         0.421937 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23        1            568.186         0.39585  │
│ trial_8d928    TERMINATED           2   adam            tanh                                   16                 96                  5                 0          0.000155258         25        1            532.574         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16        1            579.778         0.360474 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 96                  5                 0          0.00401198          24        1            542.744         0.262846 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m 367/1093[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 14ms/step - accuracy: 0.4851 - loss: 1.3740
[1m 372/1093[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 14ms/step - accuracy: 0.4851 - loss: 1.3742 [32m [repeated 49x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[1m543/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 26ms/step - accuracy: 0.4946 - loss: 1.4266[32m [repeated 51x across cluster][0m
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m23s[0m 21ms/step - accuracy: 0.3366 - loss: 1.8464 - val_accuracy: 0.3711 - val_loss: 1.6278
[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 13/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 50ms/step - accuracy: 0.4375 - loss: 1.8563
[1m   7/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 9ms/step - accuracy: 0.3896 - loss: 1.8471  
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 26ms/step - accuracy: 0.4841 - loss: 1.4310 - val_accuracy: 0.3990 - val_loss: 1.6227
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 54ms/step - accuracy: 0.4688 - loss: 1.5143
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 16/29[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 80ms/step - accuracy: 0.5312 - loss: 1.6092
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m1032/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 13ms/step - accuracy: 0.4826 - loss: 1.3830
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 398/1093[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m11s[0m 16ms/step - accuracy: 0.3457 - loss: 1.8172[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 23ms/step - accuracy: 0.4725 - loss: 1.4136 - val_accuracy: 0.4413 - val_loss: 1.5085
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:32[0m 85ms/step - accuracy: 0.6250 - loss: 0.9231
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m  43/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 20ms/step - accuracy: 0.5125 - loss: 1.3877
[1m  46/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 20ms/step - accuracy: 0.5103 - loss: 1.3896[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m402/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5189 - loss: 1.3960
[1m405/547[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5189 - loss: 1.3959[32m [repeated 59x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m Epoch 11/24
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 479ms/step
[1m 8/75[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step   
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m15/75[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[1m22/75[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m29/75[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[1m37/75[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m73/75[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1266753)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[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=1266753)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266753)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:43:58. Total running time: 11min 14s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             671.296 │
│ time_total_s                 671.296 │
│ training_iteration                 1 │
│ val_accuracy                 0.40316 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:43:58. Total running time: 11min 14s
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 19/27
[36m(train_cnn_ray_tune pid=1266753)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1085/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 15ms/step - accuracy: 0.3407 - loss: 1.8177
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m114/547[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.5231 - loss: 1.3567 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 17/29
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 18ms/step - accuracy: 0.3407 - loss: 1.8178 - val_accuracy: 0.3755 - val_loss: 1.6148
[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 14/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:37[0m 90ms/step - accuracy: 0.5625 - loss: 1.7343
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   5/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 17ms/step - accuracy: 0.4575 - loss: 1.9516 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m245/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.5239 - loss: 1.3495
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 326/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m12s[0m 16ms/step - accuracy: 0.3386 - loss: 1.8407
[1m 330/1093[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m12s[0m 16ms/step - accuracy: 0.3386 - loss: 1.8404[32m [repeated 32x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m470/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 23ms/step - accuracy: 0.5234 - loss: 1.3485
[1m473/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 23ms/step - accuracy: 0.5234 - loss: 1.3485[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 23ms/step - accuracy: 0.4953 - loss: 1.4008 - val_accuracy: 0.3907 - val_loss: 1.6357
[36m(train_cnn_ray_tune pid=1266761)[0m Epoch 20/27
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 76ms/step - accuracy: 0.4375 - loss: 1.3499
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[1m 437/1093[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 15ms/step - accuracy: 0.3384 - loss: 1.8345
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[1m 38/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 18ms/step - accuracy: 0.5439 - loss: 1.2995
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m1010/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 18ms/step - accuracy: 0.4890 - loss: 1.3813
[1m1014/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 18ms/step - accuracy: 0.4890 - loss: 1.3813[32m [repeated 33x across cluster][0m
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 409/1093[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m10s[0m 15ms/step - accuracy: 0.3384 - loss: 1.8358[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m 64/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 20ms/step - accuracy: 0.5297 - loss: 1.3409[32m [repeated 15x across cluster][0m

Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-08 17:44:15. Total running time: 11min 31s
Logical resource usage: 4.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24                                              │
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27                                              │
│ trial_8d928    RUNNING              3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           3   adam            tanh                                   16                 64                  5                 0          0.00056442          25        1            652.044         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22        1            671.296         0.403162 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25        1            542.944         0.382806 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15        1            606.726         0.43083  │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18        1            559.126         0.440909 │
│ trial_8d928    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19        1            613.609         0.421937 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23        1            568.186         0.39585  │
│ trial_8d928    TERMINATED           2   adam            tanh                                   16                 96                  5                 0          0.000155258         25        1            532.574         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16        1            579.778         0.360474 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 96                  5                 0          0.00401198          24        1            542.744         0.262846 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 423/1093[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m10s[0m 15ms/step - accuracy: 0.3384 - loss: 1.8351
[1m 427/1093[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m10s[0m 15ms/step - accuracy: 0.3384 - loss: 1.8349[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m239/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 17ms/step - accuracy: 0.5245 - loss: 1.3504
[1m242/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 17ms/step - accuracy: 0.5244 - loss: 1.3505[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m22s[0m 20ms/step - accuracy: 0.4889 - loss: 1.3813 - val_accuracy: 0.4318 - val_loss: 1.5144
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 25ms/step - accuracy: 0.5236 - loss: 1.3486 - val_accuracy: 0.3844 - val_loss: 1.6684
[36m(train_cnn_ray_tune pid=1266734)[0m Epoch 18/29
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 60ms/step - accuracy: 0.4688 - loss: 1.3320
[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m 16/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.5024 - loss: 1.3718
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 591ms/step
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m49/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[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=1266763)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1266763)[0m 
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m119/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m133/159[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m147/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 8ms/step
[1m154/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1266763)[0m 
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

Trial trial_8d928 finished iteration 1 at 2025-11-08 17:44:20. Total running time: 11min 35s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             692.693 │
│ time_total_s                 692.693 │
│ training_iteration                 1 │
│ val_accuracy                 0.43182 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:44:20. Total running time: 11min 35s
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m467/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 18ms/step - accuracy: 0.5218 - loss: 1.3563[32m [repeated 50x across cluster][0m
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 15/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1090/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 14ms/step - accuracy: 0.3392 - loss: 1.8233
[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 20ms/step - accuracy: 0.5209 - loss: 1.3582 - val_accuracy: 0.3826 - val_loss: 1.6426
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 55ms/step - accuracy: 0.2500 - loss: 1.7169
[1m   7/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 9ms/step - accuracy: 0.3186 - loss: 1.7297  
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m  13/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 9ms/step - accuracy: 0.3266 - loss: 1.7466
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
[1m 50/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:44:24. Total running time: 11min 40s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             697.155 │
│ time_total_s                 697.155 │
│ training_iteration                 1 │
│ val_accuracy                 0.38261 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:44:24. Total running time: 11min 40s
[36m(train_cnn_ray_tune pid=1266761)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:44:25. Total running time: 11min 41s
[36m(train_cnn_ray_tune pid=1266734)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1266734)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              698.33 │
│ time_total_s                  698.33 │
│ training_iteration                 1 │
│ val_accuracy                 0.38063 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266746)[0m 
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Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:44:25. Total running time: 11min 41s
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 768/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 5ms/step - accuracy: 0.3429 - loss: 1.7899
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 888/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 5ms/step - accuracy: 0.3432 - loss: 1.7893
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 5ms/step - accuracy: 0.3432 - loss: 1.7887 - val_accuracy: 0.3804 - val_loss: 1.5807
[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 16/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8097
[36m(train_cnn_ray_tune pid=1266734)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266734)[0m 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 17/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 18/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 19/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1066/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step - accuracy: 0.3683 - loss: 1.7163
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 4ms/step - accuracy: 0.3682 - loss: 1.7164 - val_accuracy: 0.3864 - val_loss: 1.5454
[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 20/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9115
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m  76/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 3ms/step - accuracy: 0.3709 - loss: 1.7040
[1m  91/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 3ms/step - accuracy: 0.3673 - loss: 1.7072

Trial status: 19 TERMINATED | 1 RUNNING
Current time: 2025-11-08 17:44:45. Total running time: 12min 1s
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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    RUNNING              3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23                                              │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           3   adam            tanh                                   16                 64                  5                 0          0.00056442          25        1            652.044         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22        1            671.296         0.403162 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25        1            542.944         0.382806 │
│ trial_8d928    TERMINATED           3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24        1            692.693         0.431818 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15        1            606.726         0.43083  │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18        1            559.126         0.440909 │
│ trial_8d928    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19        1            613.609         0.421937 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23        1            568.186         0.39585  │
│ trial_8d928    TERMINATED           2   adam            tanh                                   16                 96                  5                 0          0.000155258         25        1            532.574         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16        1            579.778         0.360474 │
│ trial_8d928    TERMINATED           3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27        1            697.155         0.382609 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 96                  5                 0          0.00401198          24        1            542.744         0.262846 │
│ trial_8d928    TERMINATED           3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29        1            698.33          0.380632 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1266746)[0m 
[1m 106/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 3ms/step - accuracy: 0.3654 - loss: 1.7090
[1m 121/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 3ms/step - accuracy: 0.3635 - loss: 1.7111
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 21/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 22/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m Epoch 23/23
[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[0m 
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[36m(train_cnn_ray_tune pid=1266746)[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=1266746)[0m   _log_deprecation_warning(
2025-11-08 17:45:03,592	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_CAPTURE24_acc_17_classes/CAPTURE24_hyperparameters_tuning' in 0.0060s.
I0000 00:00:1762620303.724013 1265113 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
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Trial trial_8d928 finished iteration 1 at 2025-11-08 17:45:03. Total running time: 12min 19s
╭──────────────────────────────────────╮
│ Trial trial_8d928 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             736.317 │
│ time_total_s                 736.317 │
│ training_iteration                 1 │
│ val_accuracy                 0.39625 │
╰──────────────────────────────────────╯

Trial trial_8d928 completed after 1 iterations at 2025-11-08 17:45:03. Total running time: 12min 19s
[36m(train_cnn_ray_tune pid=1266746)[0m 
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Trial status: 20 TERMINATED
Current time: 2025-11-08 17:45:03. Total running time: 12min 19s
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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.000626191         28        1            248.58          0.414427 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 96                  3                 1          0.00382893          23        1            288.99          0.428854 │
│ trial_8d928    TERMINATED           3   adam            tanh                                   16                 64                  5                 0          0.00056442          25        1            652.044         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          4.9456e-05          22        1            671.296         0.403162 │
│ trial_8d928    TERMINATED           2   adam            relu                                   16                 32                  5                 1          0.00290016          15        1            451.448         0.3917   │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 32                  3                 1          0.00214543          25        1            542.944         0.382806 │
│ trial_8d928    TERMINATED           3   adam            relu                                   16                 64                  3                 0          3.65104e-05         24        1            692.693         0.431818 │
│ trial_8d928    TERMINATED           2   adam            relu                                   32                 32                  3                 0          0.000176167         29        1            339.975         0.425494 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          1.33157e-05         15        1            606.726         0.43083  │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   16                 64                  3                 1          0.000995815         18        1            559.126         0.440909 │
│ trial_8d928    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 0          4.94522e-05         19        1            613.609         0.421937 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 64                  5                 1          0.00133923          20        1            306.236         0.395455 │
│ trial_8d928    TERMINATED           3   rmsprop         tanh                                   16                 64                  3                 1          0.000588481         23        1            568.186         0.39585  │
│ trial_8d928    TERMINATED           2   adam            tanh                                   16                 96                  5                 0          0.000155258         25        1            532.574         0.403557 │
│ trial_8d928    TERMINATED           2   rmsprop         tanh                                   16                 32                  3                 0          0.00471677          16        1            579.778         0.360474 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   16                 32                  3                 1          1.08587e-05         23        1            736.317         0.396245 │
│ trial_8d928    TERMINATED           3   adam            tanh                                   32                 64                  5                 0          2.3867e-05          27        1            697.155         0.382609 │
│ trial_8d928    TERMINATED           2   adam            tanh                                   32                 96                  5                 0          0.00401198          24        1            542.744         0.262846 │
│ trial_8d928    TERMINATED           3   adam            tanh                                   32                 96                  3                 1          2.29107e-05         29        1            698.33          0.380632 │
│ trial_8d928    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 1          0.000243719         23        1            367.875         0.420356 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 3, 'optimizador': 'rmsprop', 'funcion_activacion': 'relu', 'tamanho_minilote': 16, 'numero_filtros': 64, 'tamanho_filtro': 3, 'num_resblocks': 1, 'tasa_aprendizaje': 0.000995815308749953, 'epochs': 18}
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620306.144915 1299144 service.cc:152] XLA service 0x7d3a54014450 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620306.144969 1299144 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:45:06.191881: 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 00:00:1762620306.510706 1299144 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620309.106600 1299144 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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Epoch 9/18

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Saved model to disk.
2025-11-08 17:45:48.227274: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:45:48.238773: 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 00:00:1762620348.252207 1301029 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620348.256692 1301029 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620348.267141 1301029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620348.267166 1301029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620348.267168 1301029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620348.267170 1301029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:45:48.270549: 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.
I0000 00:00:1762620350.514104 1301029 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620352.891857 1301165 service.cc:152] XLA service 0x7fd9dc017aa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620352.891894 1301165 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:45:52.942645: 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 00:00:1762620353.255744 1301165 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620355.761474 1301165 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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[1m 964/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5650 - loss: 1.1262
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[1m1024/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5651 - loss: 1.1261
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 652ms/step
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 958us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

=== 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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 918us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 54/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 945us/step
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step  
[1m155/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 984us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 41.01 [%]
Global F1 score (validation) = 39.1 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[2.7480221e-03 2.4019873e-03 2.0104328e-03 ... 2.4763357e-03
  3.7289115e-03 7.6876546e-04]
 [2.1966696e-03 4.3347492e-03 3.7071961e-03 ... 6.8461038e-03
  6.1911684e-03 3.2384612e-03]
 [3.5879647e-03 3.3406629e-03 4.4616847e-03 ... 6.0448321e-03
  4.5372029e-03 3.2704920e-03]
 ...
 [8.0851314e-06 3.6532738e-06 3.1740587e-06 ... 1.5169362e-06
  6.4887899e-06 3.4189201e-05]
 [1.7961032e-05 1.2941991e-05 1.2104242e-05 ... 4.3419836e-06
  3.2157441e-05 7.7761033e-05]
 [4.5184572e-03 6.7570042e-03 5.3778868e-03 ... 2.8433269e-01
  2.3416545e-02 4.7482736e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 64.59 [%]
Global accuracy score (test) = 44.61 [%]
Global F1 score (train) = 63.83 [%]
Global F1 score (test) = 42.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.20      0.20       161
 CAMINAR CON MÓVIL O LIBRO       0.24      0.44      0.31       161
       CAMINAR USUAL SPEED       0.25      0.16      0.19       161
            CAMINAR ZIGZAG       0.11      0.09      0.10       161
          DE PIE BARRIENDO       0.64      0.37      0.47       161
   DE PIE DOBLANDO TOALLAS       0.41      0.47      0.43       161
    DE PIE MOVIENDO LIBROS       0.24      0.11      0.15       161
          DE PIE USANDO PC       0.54      0.90      0.67       161
        FASE REPOSO CON K5       0.86      0.86      0.86       161
INCREMENTAL CICLOERGOMETRO       0.91      0.96      0.93       161
           SENTADO LEYENDO       0.30      0.08      0.13       161
         SENTADO USANDO PC       0.34      0.68      0.45       161
      SENTADO VIENDO LA TV       0.25      0.14      0.18       161
   SUBIR Y BAJAR ESCALERAS       0.48      0.61      0.54       161
                    TROTAR       0.90      0.67      0.77       138

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


Accuracy capturado en la ejecución 1: 44.61 [%]
F1-score capturado en la ejecución 1: 42.56 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-08 17:46:30.232118: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:46:30.243504: 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 00:00:1762620390.256898 1302890 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620390.261230 1302890 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620390.271531 1302890 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620390.271551 1302890 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620390.271553 1302890 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620390.271555 1302890 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:46:30.274838: 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.
I0000 00:00:1762620392.531347 1302890 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620394.865836 1303022 service.cc:152] XLA service 0x7c17d0014f20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620394.865867 1303022 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:46:34.911367: 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 00:00:1762620395.210695 1303022 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620397.725295 1303022 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21:49[0m 4s/step - accuracy: 0.1875 - loss: 3.0340
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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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[1m1052/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5118 - loss: 1.2786
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Epoch 6/18

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.7500 - loss: 0.8291
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[1m 834/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5265 - loss: 1.2335
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[1m1011/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5285 - loss: 1.2304
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Epoch 7/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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Global accuracy score (validation) = 41.6 [%]
Global F1 score (validation) = 39.14 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.12880173e-03 1.35729439e-03 1.37170660e-03 ... 6.91139547e-04
  7.31301378e-04 1.09189935e-03]
 [7.47050298e-03 1.04482742e-02 1.05578396e-02 ... 6.61425246e-03
  1.03939092e-02 2.86381016e-03]
 [2.00786977e-03 1.96016999e-03 2.91758240e-03 ... 1.36699493e-03
  1.56843429e-03 1.23981398e-03]
 ...
 [8.02740396e-05 1.33052541e-04 1.77348309e-04 ... 1.88531340e-04
  4.54957364e-04 1.04981336e-04]
 [3.35546283e-05 3.90434689e-05 8.72312812e-05 ... 1.16207419e-04
  1.28978369e-04 1.48180348e-04]
 [7.74657074e-03 4.89183655e-03 4.90947720e-03 ... 3.23849410e-01
  3.30249011e-03 1.55739428e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 62.23 [%]
Global accuracy score (test) = 42.47 [%]
Global F1 score (train) = 60.2 [%]
Global F1 score (test) = 41.27 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.15      0.14      0.14       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.39      0.28       161
       CAMINAR USUAL SPEED       0.24      0.24      0.24       161
            CAMINAR ZIGZAG       0.09      0.06      0.07       161
          DE PIE BARRIENDO       0.52      0.35      0.42       161
   DE PIE DOBLANDO TOALLAS       0.32      0.61      0.42       161
    DE PIE MOVIENDO LIBROS       0.33      0.02      0.05       161
          DE PIE USANDO PC       0.61      0.71      0.65       161
        FASE REPOSO CON K5       0.83      0.71      0.77       161
INCREMENTAL CICLOERGOMETRO       0.97      0.94      0.96       161
           SENTADO LEYENDO       0.38      0.58      0.46       161
         SENTADO USANDO PC       0.17      0.17      0.17       161
      SENTADO VIENDO LA TV       0.35      0.16      0.21       161
   SUBIR Y BAJAR ESCALERAS       0.54      0.61      0.58       161
                    TROTAR       0.83      0.74      0.78       138

                  accuracy                           0.42      2392
                 macro avg       0.44      0.43      0.41      2392
              weighted avg       0.43      0.42      0.41      2392


Accuracy capturado en la ejecución 2: 42.47 [%]
F1-score capturado en la ejecución 2: 41.27 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-08 17:47:10.270912: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:47:10.282394: 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 00:00:1762620430.295598 1304646 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620430.299781 1304646 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620430.309641 1304646 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620430.309661 1304646 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620430.309672 1304646 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620430.309674 1304646 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:47:10.312927: 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.
I0000 00:00:1762620432.560014 1304646 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620434.870452 1304779 service.cc:152] XLA service 0x715770014970 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620434.870492 1304779 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:47:14.916974: 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 00:00:1762620435.233960 1304779 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620437.744903 1304779 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 20ms/step - accuracy: 0.2500 - loss: 1.6786
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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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[1m 462/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5341 - loss: 1.1850
[1m 491/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5340 - loss: 1.1861
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 657ms/step
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:39[0m 841ms/step
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[1m56/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 915us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m105/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 966us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 42.02 [%]
Global F1 score (validation) = 41.34 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.00177554 0.00406662 0.00330721 ... 0.00322037 0.00241631 0.00243346]
 [0.00279724 0.00893935 0.01280399 ... 0.00331271 0.00182936 0.00079623]
 [0.00254287 0.00593966 0.00740841 ... 0.00280423 0.00159563 0.00108178]
 ...
 [0.00237496 0.00135572 0.00205995 ... 0.00986728 0.0028902  0.00594599]
 [0.00077622 0.0006295  0.00100625 ... 0.00184958 0.00097788 0.00623326]
 [0.00353036 0.00285192 0.00581518 ... 0.24888648 0.01164004 0.01082451]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 61.77 [%]
Global accuracy score (test) = 41.39 [%]
Global F1 score (train) = 61.39 [%]
Global F1 score (test) = 40.88 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.42      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.29      0.14      0.18       161
       CAMINAR USUAL SPEED       0.28      0.20      0.24       161
            CAMINAR ZIGZAG       0.14      0.14      0.14       161
          DE PIE BARRIENDO       0.42      0.32      0.36       161
   DE PIE DOBLANDO TOALLAS       0.27      0.34      0.30       161
    DE PIE MOVIENDO LIBROS       0.31      0.21      0.25       161
          DE PIE USANDO PC       0.59      0.69      0.64       161
        FASE REPOSO CON K5       0.77      0.88      0.82       161
INCREMENTAL CICLOERGOMETRO       1.00      0.93      0.96       161
           SENTADO LEYENDO       0.33      0.60      0.42       161
         SENTADO USANDO PC       0.21      0.19      0.20       161
      SENTADO VIENDO LA TV       0.12      0.02      0.03       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.53      0.58       161
                    TROTAR       0.93      0.64      0.76       138

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


Accuracy capturado en la ejecución 3: 41.39 [%]
F1-score capturado en la ejecución 3: 40.88 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-08 17:47:47.933244: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:47:47.944587: 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 00:00:1762620467.958108 1306291 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620467.962271 1306291 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620467.972133 1306291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620467.972153 1306291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620467.972155 1306291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620467.972158 1306291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:47:47.975287: 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.
I0000 00:00:1762620470.201841 1306291 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620472.547728 1306400 service.cc:152] XLA service 0x7f780c0028c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620472.547764 1306400 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:47:52.600691: 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 00:00:1762620472.900479 1306400 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620475.384717 1306400 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 990us/step
[1m152/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 43.48 [%]
Global F1 score (validation) = 39.23 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[9.2990501e-03 8.6693643e-03 5.6462847e-03 ... 6.5699238e-03
  7.0698536e-03 4.1256165e-03]
 [6.9648596e-03 7.7258432e-03 6.2867831e-03 ... 9.5995907e-03
  3.4460558e-03 1.3114075e-03]
 [7.5592282e-03 6.3055945e-03 5.0103455e-03 ... 7.3032430e-03
  4.1796821e-03 1.7753140e-03]
 ...
 [1.6755806e-04 1.5487250e-04 8.9881047e-05 ... 1.6677706e-03
  3.8607509e-04 2.1817062e-04]
 [1.5148491e-04 1.2514771e-04 7.0446556e-05 ... 1.6344800e-03
  3.4946288e-04 3.2930737e-04]
 [1.2771705e-03 8.9922018e-04 1.3731571e-03 ... 4.2001781e-01
  6.2781437e-03 6.5511040e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 58.74 [%]
Global accuracy score (test) = 44.11 [%]
Global F1 score (train) = 56.1 [%]
Global F1 score (test) = 41.54 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.22      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.16      0.03      0.05       161
       CAMINAR USUAL SPEED       0.20      0.07      0.10       161
            CAMINAR ZIGZAG       0.18      0.53      0.27       161
          DE PIE BARRIENDO       0.55      0.65      0.59       161
   DE PIE DOBLANDO TOALLAS       0.45      0.49      0.47       161
    DE PIE MOVIENDO LIBROS       1.00      0.04      0.07       161
          DE PIE USANDO PC       0.62      0.90      0.74       161
        FASE REPOSO CON K5       0.67      0.89      0.76       161
INCREMENTAL CICLOERGOMETRO       0.92      0.96      0.94       161
           SENTADO LEYENDO       0.65      0.24      0.35       161
         SENTADO USANDO PC       0.18      0.17      0.18       161
      SENTADO VIENDO LA TV       0.13      0.15      0.14       161
   SUBIR Y BAJAR ESCALERAS       0.46      0.53      0.49       161
                    TROTAR       0.89      0.80      0.84       138

                  accuracy                           0.44      2392
                 macro avg       0.49      0.44      0.42      2392
              weighted avg       0.48      0.44      0.41      2392


Accuracy capturado en la ejecución 4: 44.11 [%]
F1-score capturado en la ejecución 4: 41.54 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-08 17:48:27.938455: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:48:27.949937: 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 00:00:1762620507.963460 1308045 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620507.967837 1308045 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620507.977591 1308045 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620507.977615 1308045 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620507.977617 1308045 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620507.977619 1308045 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:48:27.980852: 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.
I0000 00:00:1762620510.220114 1308045 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620512.534382 1308155 service.cc:152] XLA service 0x7e1004002550 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620512.534413 1308155 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:48:32.580186: 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 00:00:1762620512.879471 1308155 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620515.389384 1308155 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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Epoch 9/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 644ms/step
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 50/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m104/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 974us/step
[1m158/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 963us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 41.19 [%]
Global F1 score (validation) = 37.28 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[2.8564264e-03 1.5448484e-03 1.6067247e-03 ... 3.7690431e-03
  1.5502746e-03 1.5909782e-03]
 [8.3367871e-03 4.6078060e-03 7.8197392e-03 ... 6.0965382e-03
  6.1498703e-03 2.2809687e-03]
 [5.8504003e-03 2.2455296e-03 4.9220575e-03 ... 2.9651325e-03
  3.5467520e-03 1.9010728e-03]
 ...
 [2.4807632e-05 7.1030868e-06 1.8612975e-05 ... 3.2927081e-05
  8.2720260e-05 7.4770354e-04]
 [4.4495253e-05 1.2721857e-05 2.6386788e-05 ... 1.3904362e-04
  9.1775059e-05 3.1582400e-04]
 [3.7230849e-03 1.5277017e-03 8.7515987e-04 ... 2.9066366e-01
  1.6157694e-02 2.4016160e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 59.37 [%]
Global accuracy score (test) = 45.94 [%]
Global F1 score (train) = 57.74 [%]
Global F1 score (test) = 44.4 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.16      0.20      0.18       161
 CAMINAR CON MÓVIL O LIBRO       0.27      0.14      0.18       161
       CAMINAR USUAL SPEED       0.38      0.15      0.21       161
            CAMINAR ZIGZAG       0.18      0.40      0.25       161
          DE PIE BARRIENDO       0.44      0.56      0.49       161
   DE PIE DOBLANDO TOALLAS       0.39      0.17      0.23       161
    DE PIE MOVIENDO LIBROS       0.17      0.11      0.13       161
          DE PIE USANDO PC       0.53      0.86      0.65       161
        FASE REPOSO CON K5       0.86      0.88      0.87       161
INCREMENTAL CICLOERGOMETRO       0.96      0.95      0.95       161
           SENTADO LEYENDO       0.62      0.43      0.51       161
         SENTADO USANDO PC       0.40      0.75      0.52       161
      SENTADO VIENDO LA TV       0.34      0.08      0.13       161
   SUBIR Y BAJAR ESCALERAS       0.50      0.54      0.52       161
                    TROTAR       0.96      0.70      0.81       138

                  accuracy                           0.46      2392
                 macro avg       0.48      0.46      0.44      2392
              weighted avg       0.47      0.46      0.44      2392


Accuracy capturado en la ejecución 5: 45.94 [%]
F1-score capturado en la ejecución 5: 44.4 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-08 17:49:12.291061: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:49:12.302709: 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 00:00:1762620552.316072 1310005 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620552.320208 1310005 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620552.329922 1310005 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620552.329941 1310005 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620552.329943 1310005 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620552.329945 1310005 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:49:12.333120: 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.
I0000 00:00:1762620554.585219 1310005 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620556.920719 1310137 service.cc:152] XLA service 0x7b85e40284e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620556.920767 1310137 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:49:16.974999: 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 00:00:1762620557.286612 1310137 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620559.784377 1310137 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:22:03[0m 5s/step - accuracy: 0.0000e+00 - loss: 3.7226
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[1m  53/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 2ms/step - accuracy: 0.1979 - loss: 2.6319
[1m  78/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2073 - loss: 2.5437
[1m 110/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2132 - loss: 2.4727
[1m 139/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2183 - loss: 2.4245
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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m154/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 992us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 41.92 [%]
Global F1 score (validation) = 39.26 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.4199588e-03 6.4940462e-03 2.9806462e-03 ... 4.6953927e-03
  8.0446377e-03 3.1059210e-03]
 [6.4947191e-03 1.6932592e-02 5.4322411e-03 ... 7.2635678e-03
  9.8582357e-03 1.1362948e-03]
 [5.7516657e-03 6.3196742e-03 3.7166066e-03 ... 5.1855608e-03
  7.4102287e-03 2.0452577e-03]
 ...
 [2.0136520e-04 6.4270059e-04 6.3025818e-04 ... 4.0605126e-05
  3.2578697e-05 1.5667704e-04]
 [2.1224172e-04 8.2662422e-04 6.5476506e-04 ... 1.1017741e-04
  4.8914946e-05 1.8485730e-04]
 [1.2754655e-03 9.2072441e-04 1.8064977e-03 ... 3.6578545e-01
  9.0414910e-03 2.2434411e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 65.39 [%]
Global accuracy score (test) = 45.48 [%]
Global F1 score (train) = 63.85 [%]
Global F1 score (test) = 43.38 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.30      0.26       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.33      0.29       161
       CAMINAR USUAL SPEED       0.27      0.25      0.26       161
            CAMINAR ZIGZAG       0.19      0.19      0.19       161
          DE PIE BARRIENDO       0.44      0.57      0.50       161
   DE PIE DOBLANDO TOALLAS       0.38      0.34      0.36       161
    DE PIE MOVIENDO LIBROS       0.22      0.12      0.15       161
          DE PIE USANDO PC       0.64      0.80      0.71       161
        FASE REPOSO CON K5       0.86      0.85      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.26      0.09      0.13       161
      SENTADO VIENDO LA TV       0.33      0.83      0.47       161
   SUBIR Y BAJAR ESCALERAS       0.59      0.50      0.54       161
                    TROTAR       0.91      0.75      0.83       138

                  accuracy                           0.45      2392
                 macro avg       0.44      0.46      0.43      2392
              weighted avg       0.43      0.45      0.43      2392


Accuracy capturado en la ejecución 6: 45.48 [%]
F1-score capturado en la ejecución 6: 43.38 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-08 17:49:52.131936: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:49:52.143649: 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 00:00:1762620592.157264 1311771 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620592.161583 1311771 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620592.171530 1311771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620592.171550 1311771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620592.171552 1311771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620592.171554 1311771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:49:52.174743: 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.
I0000 00:00:1762620594.408156 1311771 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620596.769653 1311902 service.cc:152] XLA service 0x7f70b4014710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620596.769712 1311902 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:49:56.817436: 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 00:00:1762620597.130091 1311902 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620599.634222 1311902 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 669ms/step
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m102/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m158/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 966us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 41.42 [%]
Global F1 score (validation) = 39.47 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.0037623  0.00189395 0.00114807 ... 0.00182876 0.00185369 0.00112581]
 [0.00553803 0.0024155  0.0034126  ... 0.00368421 0.00319442 0.00046149]
 [0.00622078 0.00212507 0.00322844 ... 0.00327947 0.0028976  0.00043218]
 ...
 [0.00060458 0.00089874 0.00211306 ... 0.00268554 0.00042184 0.00061508]
 [0.00041687 0.00054629 0.00125266 ... 0.00201228 0.00042971 0.00072043]
 [0.00373998 0.00720261 0.01226652 ... 0.24329141 0.01294351 0.00442534]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 59.62 [%]
Global accuracy score (test) = 42.02 [%]
Global F1 score (train) = 57.89 [%]
Global F1 score (test) = 39.2 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.17      0.18       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.60      0.35       161
       CAMINAR USUAL SPEED       0.31      0.27      0.29       161
            CAMINAR ZIGZAG       0.00      0.00      0.00       161
          DE PIE BARRIENDO       0.36      0.30      0.33       161
   DE PIE DOBLANDO TOALLAS       0.33      0.32      0.33       161
    DE PIE MOVIENDO LIBROS       0.15      0.07      0.09       161
          DE PIE USANDO PC       0.51      0.93      0.66       161
        FASE REPOSO CON K5       0.86      0.86      0.86       161
INCREMENTAL CICLOERGOMETRO       0.94      0.96      0.95       161
           SENTADO LEYENDO       0.30      0.49      0.37       161
         SENTADO USANDO PC       0.26      0.30      0.28       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.43      0.48      0.46       161
                    TROTAR       0.96      0.58      0.72       138

                  accuracy                           0.42      2392
                 macro avg       0.39      0.42      0.39      2392
              weighted avg       0.39      0.42      0.39      2392


Accuracy capturado en la ejecución 7: 42.02 [%]
F1-score capturado en la ejecución 7: 39.2 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-08 17:50:29.730763: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:50:29.742151: 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 00:00:1762620629.755330 1313421 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620629.759570 1313421 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620629.769392 1313421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620629.769410 1313421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620629.769412 1313421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620629.769413 1313421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:50:29.772638: 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.
I0000 00:00:1762620632.031792 1313421 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620634.392540 1313531 service.cc:152] XLA service 0x7df938014680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620634.392572 1313531 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:50:34.438089: 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 00:00:1762620634.738629 1313531 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620637.250917 1313531 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m155/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 983us/step
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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 56/159[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 916us/step
[1m106/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 963us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 40.24 [%]
Global F1 score (validation) = 36.69 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[9.1133062e-03 3.3277026e-03 3.8198533e-03 ... 5.2432227e-04
  1.5887826e-03 1.3255414e-03]
 [1.1388268e-02 7.0418445e-03 6.9976710e-03 ... 1.9382812e-03
  2.3317218e-03 3.0858244e-03]
 [1.1615854e-02 5.8176406e-03 6.0371393e-03 ... 1.3335075e-03
  2.3812822e-03 2.7120039e-03]
 ...
 [2.1420735e-04 2.3281144e-04 1.0842143e-04 ... 5.0174794e-04
  7.0734284e-05 6.6482875e-04]
 [6.1451481e-04 6.2180770e-04 2.7690164e-04 ... 1.1252036e-03
  1.6665633e-04 1.1243109e-03]
 [1.0674237e-02 6.4708125e-03 8.0514820e-03 ... 2.3722517e-01
  7.8283446e-03 4.4545764e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 52.61 [%]
Global accuracy score (test) = 42.93 [%]
Global F1 score (train) = 49.77 [%]
Global F1 score (test) = 39.28 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.40      0.27       161
 CAMINAR CON MÓVIL O LIBRO       0.17      0.10      0.12       161
       CAMINAR USUAL SPEED       0.30      0.22      0.25       161
            CAMINAR ZIGZAG       0.12      0.09      0.10       161
          DE PIE BARRIENDO       0.45      0.85      0.59       161
   DE PIE DOBLANDO TOALLAS       0.29      0.31      0.30       161
    DE PIE MOVIENDO LIBROS       0.14      0.12      0.13       161
          DE PIE USANDO PC       0.00      0.00      0.00       161
        FASE REPOSO CON K5       0.86      0.86      0.86       161
INCREMENTAL CICLOERGOMETRO       1.00      0.95      0.97       161
           SENTADO LEYENDO       0.39      0.86      0.54       161
         SENTADO USANDO PC       0.14      0.04      0.07       161
      SENTADO VIENDO LA TV       0.70      0.25      0.37       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.61      0.56       161
                    TROTAR       0.68      0.83      0.75       138

                  accuracy                           0.43      2392
                 macro avg       0.40      0.43      0.39      2392
              weighted avg       0.40      0.43      0.39      2392


Accuracy capturado en la ejecución 8: 42.93 [%]
F1-score capturado en la ejecución 8: 39.28 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-08 17:51:09.581761: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:51:09.593269: 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 00:00:1762620669.606544 1315159 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620669.610666 1315159 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620669.620592 1315159 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620669.620610 1315159 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620669.620612 1315159 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620669.620614 1315159 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:51:09.623844: 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.
I0000 00:00:1762620671.865070 1315159 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620674.199777 1315288 service.cc:152] XLA service 0x74701c014380 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620674.199806 1315288 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:51:14.245500: 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 00:00:1762620674.544459 1315288 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620677.078888 1315288 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 643ms/step
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:52[0m 865ms/step
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[1m45/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m107/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 951us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 41.23 [%]
Global F1 score (validation) = 38.9 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.7872048e-03 1.6265357e-02 1.1691350e-02 ... 2.7079380e-03
  4.8158206e-03 5.6783743e-03]
 [5.9977225e-03 1.1918355e-02 1.0009506e-02 ... 6.9994484e-03
  3.3441903e-03 2.1403043e-03]
 [7.6749851e-03 1.1113429e-02 1.1317038e-02 ... 6.5306416e-03
  3.5696169e-03 2.3066034e-03]
 ...
 [5.1625381e-04 2.8967613e-04 2.0374826e-04 ... 2.7909281e-04
  2.1846639e-04 5.7962659e-04]
 [5.8824045e-04 4.8765272e-04 2.9165557e-04 ... 2.2218782e-03
  9.4201177e-04 1.3991792e-03]
 [2.6694247e-03 2.5096389e-03 2.1565307e-03 ... 3.5585850e-01
  1.8174147e-02 2.3438632e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 62.65 [%]
Global accuracy score (test) = 45.4 [%]
Global F1 score (train) = 61.43 [%]
Global F1 score (test) = 43.9 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.21      0.22       161
 CAMINAR CON MÓVIL O LIBRO       0.26      0.35      0.30       161
       CAMINAR USUAL SPEED       0.31      0.21      0.25       161
            CAMINAR ZIGZAG       0.20      0.40      0.27       161
          DE PIE BARRIENDO       0.57      0.56      0.56       161
   DE PIE DOBLANDO TOALLAS       0.57      0.26      0.36       161
    DE PIE MOVIENDO LIBROS       0.22      0.21      0.21       161
          DE PIE USANDO PC       0.55      0.83      0.66       161
        FASE REPOSO CON K5       0.77      0.87      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.41      0.73      0.53       161
      SENTADO VIENDO LA TV       0.35      0.29      0.32       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.47      0.52       161
                    TROTAR       0.84      0.47      0.60       138

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


Accuracy capturado en la ejecución 9: 45.4 [%]
F1-score capturado en la ejecución 9: 43.9 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-08 17:51:49.466375: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:51:49.477634: 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 00:00:1762620709.491143 1316906 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620709.495240 1316906 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620709.505676 1316906 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620709.505694 1316906 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620709.505696 1316906 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620709.505697 1316906 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:51:49.508696: 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.
I0000 00:00:1762620711.730970 1316906 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620714.048631 1317034 service.cc:152] XLA service 0x7a247c0068e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620714.048693 1317034 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:51:54.100068: 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 00:00:1762620714.407429 1317034 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620716.896981 1317034 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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[1m 972/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5285 - loss: 1.2293
[1m1002/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5284 - loss: 1.2293
[1m1036/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5282 - loss: 1.2292
[1m1066/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5280 - loss: 1.2292
[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.5279 - loss: 1.2292 - val_accuracy: 0.3792 - val_loss: 1.6206
Epoch 7/18

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 22ms/step - accuracy: 0.8125 - loss: 0.8755
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[1m  55/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5447 - loss: 1.1351
[1m  84/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5425 - loss: 1.1420
[1m 114/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5456 - loss: 1.1407
[1m 142/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5481 - loss: 1.1416
[1m 171/1093[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5501 - loss: 1.1423
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[1m 233/1093[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5522 - loss: 1.1449
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[1m 294/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5540 - loss: 1.1436
[1m 322/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5542 - loss: 1.1433
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[1m 410/1093[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5546 - loss: 1.1421
[1m 440/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5544 - loss: 1.1424
[1m 468/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5543 - loss: 1.1430
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[1m 525/1093[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5543 - loss: 1.1438
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[1m 584/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5541 - loss: 1.1449
[1m 615/1093[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5539 - loss: 1.1456
[1m 644/1093[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5537 - loss: 1.1462
[1m 675/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5536 - loss: 1.1469
[1m 702/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5536 - loss: 1.1473
[1m 730/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5536 - loss: 1.1478
[1m 760/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5535 - loss: 1.1484
[1m 788/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5534 - loss: 1.1489
[1m 814/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5533 - loss: 1.1493
[1m 843/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5532 - loss: 1.1498
[1m 871/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5531 - loss: 1.1502
[1m 903/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5531 - loss: 1.1506
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[1m1052/1093[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5531 - loss: 1.1519
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Epoch 8/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 54/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 947us/step
[1m112/159[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 910us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 40.71 [%]
Global F1 score (validation) = 41.2 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.3298527e-04 1.7124064e-03 4.0238193e-04 ... 2.9769778e-04
  2.0340293e-04 8.0867299e-05]
 [1.6205894e-03 4.0491843e-03 2.0284704e-03 ... 7.5921090e-04
  9.7625010e-04 1.3080992e-04]
 [1.4198448e-03 3.2607059e-03 1.9099889e-03 ... 5.4498622e-04
  7.2830601e-04 9.1391732e-05]
 ...
 [6.7001129e-05 8.4599604e-05 1.6805989e-04 ... 3.1237936e-04
  2.4732694e-04 1.6051704e-04]
 [7.5326068e-05 7.0177477e-05 2.2206341e-04 ... 4.7323087e-04
  1.2944447e-04 2.0679920e-04]
 [1.9639302e-03 1.5308005e-03 2.3258058e-03 ... 3.3954409e-01
  3.0254202e-03 2.0391370e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 62.1 [%]
Global accuracy score (test) = 44.31 [%]
Global F1 score (train) = 62.5 [%]
Global F1 score (test) = 44.44 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.13      0.11      0.12       161
 CAMINAR CON MÓVIL O LIBRO       0.33      0.46      0.39       161
       CAMINAR USUAL SPEED       0.29      0.24      0.26       161
            CAMINAR ZIGZAG       0.15      0.19      0.17       161
          DE PIE BARRIENDO       0.57      0.39      0.46       161
   DE PIE DOBLANDO TOALLAS       0.30      0.41      0.34       161
    DE PIE MOVIENDO LIBROS       0.25      0.18      0.21       161
          DE PIE USANDO PC       0.60      0.77      0.68       161
        FASE REPOSO CON K5       0.86      0.87      0.86       161
INCREMENTAL CICLOERGOMETRO       1.00      0.96      0.98       161
           SENTADO LEYENDO       0.29      0.43      0.34       161
         SENTADO USANDO PC       0.27      0.16      0.20       161
      SENTADO VIENDO LA TV       0.52      0.42      0.47       161
   SUBIR Y BAJAR ESCALERAS       0.55      0.54      0.55       161
                    TROTAR       0.81      0.53      0.64       138

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


Accuracy capturado en la ejecución 10: 44.31 [%]
F1-score capturado en la ejecución 10: 44.44 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-08 17:52:31.735061: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:52:31.746570: 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 00:00:1762620751.760058 1318781 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620751.764186 1318781 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620751.773960 1318781 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620751.773979 1318781 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620751.773981 1318781 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620751.773983 1318781 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:52:31.777126: 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.
I0000 00:00:1762620754.016416 1318781 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620756.353040 1318892 service.cc:152] XLA service 0x7561c0003000 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620756.353070 1318892 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:52:36.399525: 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 00:00:1762620756.698730 1318892 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620759.219140 1318892 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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[1m 864/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4224 - loss: 1.4850
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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 48/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 989us/step
[1m147/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 42.29 [%]
Global F1 score (validation) = 39.44 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.00211545 0.0041772  0.00158773 ... 0.00202083 0.00110397 0.00117938]
 [0.0085629  0.01042255 0.00582495 ... 0.00742184 0.00589201 0.0012643 ]
 [0.00335324 0.00300965 0.00232693 ... 0.00287917 0.00207746 0.00141864]
 ...
 [0.00064277 0.00062088 0.0013143  ... 0.00300656 0.00123521 0.0025141 ]
 [0.00092751 0.00076417 0.00213992 ... 0.00453011 0.00109195 0.004164  ]
 [0.00521192 0.00113597 0.00392724 ... 0.29128715 0.0058973  0.00176391]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 61.78 [%]
Global accuracy score (test) = 45.48 [%]
Global F1 score (train) = 59.93 [%]
Global F1 score (test) = 43.48 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.22      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.31      0.51      0.38       161
       CAMINAR USUAL SPEED       0.25      0.24      0.24       161
            CAMINAR ZIGZAG       0.18      0.20      0.19       161
          DE PIE BARRIENDO       0.46      0.59      0.52       161
   DE PIE DOBLANDO TOALLAS       0.40      0.41      0.40       161
    DE PIE MOVIENDO LIBROS       0.26      0.12      0.16       161
          DE PIE USANDO PC       0.62      0.71      0.66       161
        FASE REPOSO CON K5       0.86      0.85      0.85       161
INCREMENTAL CICLOERGOMETRO       0.97      0.93      0.95       161
           SENTADO LEYENDO       0.28      0.12      0.17       161
         SENTADO USANDO PC       0.31      0.78      0.44       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.65      0.40      0.50       161
                    TROTAR       0.84      0.78      0.81       138

                  accuracy                           0.45      2392
                 macro avg       0.44      0.46      0.43      2392
              weighted avg       0.44      0.45      0.43      2392


Accuracy capturado en la ejecución 11: 45.48 [%]
F1-score capturado en la ejecución 11: 43.48 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-08 17:53:13.788154: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:53:13.799870: 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 00:00:1762620793.813721 1320640 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620793.818007 1320640 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620793.828195 1320640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620793.828217 1320640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620793.828220 1320640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620793.828221 1320640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:53:13.831447: 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.
I0000 00:00:1762620796.101630 1320640 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620798.447877 1320771 service.cc:152] XLA service 0x793008015cb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620798.447916 1320771 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:53:18.495665: 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 00:00:1762620798.809884 1320771 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620801.322174 1320771 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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[1m  58/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1631 - loss: 2.6939
[1m  87/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1830 - loss: 2.5806
[1m 120/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1967 - loss: 2.4947
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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 23ms/step - accuracy: 0.3750 - loss: 1.1815
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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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[1m 586/1093[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5476 - loss: 1.1230
[1m 614/1093[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5483 - loss: 1.1228
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[1m 675/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5495 - loss: 1.1224
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[1m 875/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5525 - loss: 1.1211
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 656ms/step
[1m54/75[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 955us/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:51[0m 864ms/step
[1m 55/547[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 934us/step  
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[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 47/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m155/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 980us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.77 [%]
Global F1 score (validation) = 34.46 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.0580208e-03 9.0554385e-03 1.1456470e-02 ... 6.1253873e-03
  4.4871187e-03 3.6599734e-03]
 [7.0881960e-04 1.3163207e-03 3.4340471e-03 ... 5.5836223e-04
  3.2474758e-04 2.0910108e-04]
 [1.4694655e-04 3.7866129e-04 1.3103050e-03 ... 5.7255354e-05
  3.2573964e-05 3.3377422e-05]
 ...
 [4.2790518e-05 1.0216558e-04 5.2749569e-05 ... 1.0235526e-03
  4.9141796e-05 5.0811603e-05]
 [4.8294605e-05 1.0902514e-04 6.3862848e-05 ... 1.1243781e-03
  6.2961553e-05 6.7060333e-05]
 [5.2638263e-03 9.8496606e-04 1.0760403e-03 ... 3.9296034e-01
  1.3392518e-02 1.5485749e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.32 [%]
Global accuracy score (test) = 43.02 [%]
Global F1 score (train) = 54.92 [%]
Global F1 score (test) = 40.36 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.49      0.31       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.32      0.26       161
       CAMINAR USUAL SPEED       0.28      0.10      0.15       161
            CAMINAR ZIGZAG       0.25      0.08      0.12       161
          DE PIE BARRIENDO       0.65      0.09      0.16       161
   DE PIE DOBLANDO TOALLAS       0.11      0.05      0.07       161
    DE PIE MOVIENDO LIBROS       0.28      0.16      0.20       161
          DE PIE USANDO PC       0.35      0.98      0.51       161
        FASE REPOSO CON K5       0.86      0.85      0.85       161
INCREMENTAL CICLOERGOMETRO       0.97      0.89      0.93       161
           SENTADO LEYENDO       0.72      0.14      0.24       161
         SENTADO USANDO PC       0.47      0.58      0.52       161
      SENTADO VIENDO LA TV       0.37      0.49      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.46      0.57      0.51       161
                    TROTAR       0.96      0.69      0.80       138

                  accuracy                           0.43      2392
                 macro avg       0.48      0.43      0.40      2392
              weighted avg       0.47      0.43      0.40      2392


Accuracy capturado en la ejecución 12: 43.02 [%]
F1-score capturado en la ejecución 12: 40.36 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-08 17:53:56.159432: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:53:56.170711: 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 00:00:1762620836.184171 1322497 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620836.188209 1322497 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620836.198266 1322497 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620836.198286 1322497 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620836.198288 1322497 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620836.198290 1322497 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:53:56.201497: 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.
I0000 00:00:1762620838.448518 1322497 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620840.792716 1322628 service.cc:152] XLA service 0x7a21000151a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620840.792748 1322628 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:54:00.843730: 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 00:00:1762620841.167025 1322628 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620843.679264 1322628 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:45[0m 852ms/step
[1m 50/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step    
[1m103/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 985us/step
[1m160/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 951us/step
[1m211/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 958us/step
[1m264/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 955us/step
[1m317/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 954us/step
[1m370/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 953us/step
[1m419/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 963us/step
[1m474/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 959us/step
[1m529/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 955us/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
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 46/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m105/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 967us/step
[1m157/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 969us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 41.92 [%]
Global F1 score (validation) = 39.21 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[3.4243066e-03 2.0819784e-03 2.1411460e-03 ... 1.6405727e-03
  1.5753004e-03 2.2048659e-03]
 [2.4567235e-03 1.0898885e-03 1.3733809e-03 ... 1.8867088e-03
  1.3850142e-03 9.2709798e-04]
 [2.6512095e-03 8.9079555e-04 1.1787323e-03 ... 1.2558385e-03
  9.9815463e-04 9.5922966e-04]
 ...
 [6.5821172e-05 2.2525699e-05 1.1231583e-04 ... 1.2760496e-04
  7.8737321e-06 1.5275388e-04]
 [7.1435323e-05 2.8000099e-05 1.4451361e-04 ... 1.1394602e-04
  1.1996978e-05 1.6086044e-04]
 [2.4229318e-03 5.2198279e-03 1.3211977e-03 ... 2.5307390e-01
  1.2423039e-02 5.6666316e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 60.88 [%]
Global accuracy score (test) = 44.23 [%]
Global F1 score (train) = 59.04 [%]
Global F1 score (test) = 42.09 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.13      0.12      0.13       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.45      0.30       161
       CAMINAR USUAL SPEED       0.27      0.22      0.24       161
            CAMINAR ZIGZAG       0.12      0.06      0.08       161
          DE PIE BARRIENDO       0.53      0.60      0.56       161
   DE PIE DOBLANDO TOALLAS       0.11      0.01      0.02       161
    DE PIE MOVIENDO LIBROS       0.25      0.22      0.23       161
          DE PIE USANDO PC       0.47      0.93      0.63       161
        FASE REPOSO CON K5       0.86      0.88      0.87       161
INCREMENTAL CICLOERGOMETRO       0.94      0.94      0.94       161
           SENTADO LEYENDO       0.20      0.18      0.19       161
         SENTADO USANDO PC       0.31      0.39      0.34       161
      SENTADO VIENDO LA TV       0.48      0.27      0.35       161
   SUBIR Y BAJAR ESCALERAS       0.56      0.68      0.62       161
                    TROTAR       0.91      0.74      0.82       138

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


Accuracy capturado en la ejecución 13: 44.23 [%]
F1-score capturado en la ejecución 13: 42.09 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-08 17:54:36.130632: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:54:36.142103: 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 00:00:1762620876.155388 1324263 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620876.159521 1324263 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620876.169419 1324263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620876.169437 1324263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620876.169439 1324263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620876.169440 1324263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:54:36.172409: 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.
I0000 00:00:1762620878.402875 1324263 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620880.754347 1324396 service.cc:152] XLA service 0x7b918c0025e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620880.754397 1324396 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:54:40.804173: 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 00:00:1762620881.118637 1324396 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620883.612994 1324396 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:22:15[0m 5s/step - accuracy: 0.2500 - loss: 3.2052
[1m  28/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 2ms/step - accuracy: 0.1852 - loss: 2.8117    
[1m  59/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2068 - loss: 2.6174
[1m  85/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2152 - loss: 2.5321
[1m 116/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2223 - loss: 2.4622
[1m 146/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2290 - loss: 2.4099
[1m 176/1093[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2340 - loss: 2.3691
[1m 206/1093[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2384 - loss: 2.3351
[1m 235/1093[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2418 - loss: 2.3085
[1m 265/1093[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2449 - loss: 2.2845
[1m 293/1093[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2476 - loss: 2.2651
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[1m 381/1093[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2549 - loss: 2.2166
[1m 412/1093[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2570 - loss: 2.2023
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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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[1m 820/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5829 - loss: 1.1106
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[1m 875/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5827 - loss: 1.1107
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[1m 930/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5826 - loss: 1.1105
[1m 958/1093[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5826 - loss: 1.1104
[1m 987/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5825 - loss: 1.1104
[1m1017/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5825 - loss: 1.1103
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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 655ms/step
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:56[0m 872ms/step
[1m 46/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step    
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[1m157/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 967us/step
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[1m272/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 927us/step
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[1m373/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 949us/step
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[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m52/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 984us/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m 99/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m152/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1000us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step   
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.89 [%]
Global F1 score (validation) = 35.17 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[3.7883515e-03 3.6264258e-03 5.1750615e-03 ... 2.0815281e-02
  1.4353815e-02 5.5489368e-03]
 [3.1124111e-03 1.2173473e-03 3.2378586e-03 ... 1.0455459e-02
  1.0547050e-02 1.9414807e-03]
 [2.9746359e-03 7.9390570e-04 3.0093445e-03 ... 6.4747892e-03
  7.5806724e-03 1.1503373e-03]
 ...
 [2.5180978e-05 2.4689694e-05 5.2520718e-05 ... 6.9029338e-05
  2.2891349e-04 1.1459033e-03]
 [3.4298188e-05 4.0851515e-05 6.1576684e-05 ... 1.3768020e-04
  3.1187828e-04 1.2513680e-03]
 [1.2045302e-03 1.9086694e-03 1.7126955e-03 ... 2.9179221e-01
  1.2953409e-02 2.3314613e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 54.28 [%]
Global accuracy score (test) = 41.18 [%]
Global F1 score (train) = 52.63 [%]
Global F1 score (test) = 39.01 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.27      0.21       161
 CAMINAR CON MÓVIL O LIBRO       0.22      0.37      0.28       161
       CAMINAR USUAL SPEED       0.30      0.17      0.22       161
            CAMINAR ZIGZAG       0.16      0.11      0.13       161
          DE PIE BARRIENDO       0.18      0.11      0.13       161
   DE PIE DOBLANDO TOALLAS       0.28      0.22      0.25       161
    DE PIE MOVIENDO LIBROS       0.12      0.04      0.06       161
          DE PIE USANDO PC       0.33      0.84      0.47       161
        FASE REPOSO CON K5       0.86      0.89      0.88       161
INCREMENTAL CICLOERGOMETRO       0.99      0.94      0.97       161
           SENTADO LEYENDO       0.37      0.85      0.51       161
         SENTADO USANDO PC       0.96      0.14      0.24       161
      SENTADO VIENDO LA TV       0.40      0.14      0.21       161
   SUBIR Y BAJAR ESCALERAS       0.61      0.49      0.54       161
                    TROTAR       0.94      0.63      0.75       138

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


Accuracy capturado en la ejecución 14: 41.18 [%]
F1-score capturado en la ejecución 14: 39.01 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-08 17:55:18.352677: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:55:18.363896: 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 00:00:1762620918.376981 1326144 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620918.381122 1326144 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620918.390915 1326144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620918.390934 1326144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620918.390936 1326144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620918.390937 1326144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:55:18.394063: 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.
I0000 00:00:1762620920.621706 1326144 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620922.966841 1326255 service.cc:152] XLA service 0x7dde04015e90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620922.966894 1326255 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:55:23.015793: 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 00:00:1762620923.317661 1326255 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620925.834873 1326255 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 53/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 977us/step
[1m111/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 923us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 38.5 [%]
Global F1 score (validation) = 35.3 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.8596679e-03 5.9217499e-03 1.2548886e-03 ... 5.0229061e-04
  6.1072648e-04 2.8607447e-04]
 [3.9488981e-03 8.7376554e-03 3.5776529e-03 ... 1.1101348e-03
  1.0859264e-03 3.9086456e-04]
 [2.8615764e-03 5.4882411e-03 2.1030772e-03 ... 8.4526755e-04
  1.3127597e-03 4.8716739e-04]
 ...
 [6.8680056e-05 3.0218718e-05 4.1638090e-05 ... 1.3597535e-04
  4.2183019e-05 5.6116860e-05]
 [2.4890091e-04 1.2598379e-04 3.2439706e-04 ... 2.8784233e-04
  1.2356341e-04 1.7749473e-04]
 [9.1850659e-04 1.2611052e-03 4.5220889e-04 ... 3.1213322e-01
  1.4448239e-02 1.3404699e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 60.5 [%]
Global accuracy score (test) = 40.47 [%]
Global F1 score (train) = 58.35 [%]
Global F1 score (test) = 38.59 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.15      0.14      0.14       161
 CAMINAR CON MÓVIL O LIBRO       0.23      0.19      0.21       161
       CAMINAR USUAL SPEED       0.25      0.32      0.28       161
            CAMINAR ZIGZAG       0.14      0.22      0.17       161
          DE PIE BARRIENDO       0.53      0.46      0.49       161
   DE PIE DOBLANDO TOALLAS       0.34      0.29      0.31       161
    DE PIE MOVIENDO LIBROS       0.23      0.54      0.32       161
          DE PIE USANDO PC       0.00      0.00      0.00       161
        FASE REPOSO CON K5       0.86      0.89      0.87       161
INCREMENTAL CICLOERGOMETRO       1.00      0.96      0.98       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.34      0.76      0.47       161
      SENTADO VIENDO LA TV       0.31      0.14      0.19       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.48      0.53       161
                    TROTAR       0.91      0.75      0.82       138

                  accuracy                           0.40      2392
                 macro avg       0.39      0.41      0.39      2392
              weighted avg       0.39      0.40      0.38      2392


Accuracy capturado en la ejecución 15: 40.47 [%]
F1-score capturado en la ejecución 15: 38.59 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-08 17:56:00.682794: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:56:00.694102: 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 00:00:1762620960.707681 1328001 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620960.711820 1328001 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620960.721780 1328001 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620960.721800 1328001 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620960.721802 1328001 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620960.721803 1328001 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:56:00.724985: 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.
I0000 00:00:1762620962.992446 1328001 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762620965.289056 1328132 service.cc:152] XLA service 0x7aa6a4002400 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762620965.289092 1328132 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:56:05.341430: 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 00:00:1762620965.657707 1328132 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762620968.183079 1328132 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 22ms/step - accuracy: 0.3125 - loss: 1.9451
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[1m 697/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3808 - loss: 1.6223
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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 665ms/step
[1m49/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 43/159[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m100/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m155/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 983us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.98 [%]
Global F1 score (validation) = 35.7 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.06429235e-03 3.63728474e-03 8.60947242e-04 ... 5.75654616e-04
  3.38468794e-03 1.02615752e-03]
 [3.90418665e-03 1.01752393e-02 3.28640407e-03 ... 2.29596300e-03
  1.75055221e-03 8.03600298e-04]
 [2.55532051e-03 3.77733097e-03 9.63762344e-04 ... 7.71367690e-04
  1.24081783e-03 4.92244202e-04]
 ...
 [1.65872683e-04 1.02717946e-04 6.67938293e-05 ... 2.02934185e-04
  8.11431382e-05 2.61169975e-04]
 [6.06883666e-04 5.01399394e-04 2.47408840e-04 ... 1.76850799e-03
  5.55072096e-04 6.61291066e-04]
 [2.28763651e-03 1.04061095e-03 1.49719988e-03 ... 3.16255391e-01
  5.69325313e-03 2.28490122e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 58.88 [%]
Global accuracy score (test) = 43.94 [%]
Global F1 score (train) = 58.39 [%]
Global F1 score (test) = 42.37 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.43      0.31       161
 CAMINAR CON MÓVIL O LIBRO       0.27      0.24      0.25       161
       CAMINAR USUAL SPEED       0.21      0.25      0.23       161
            CAMINAR ZIGZAG       0.22      0.14      0.17       161
          DE PIE BARRIENDO       0.44      0.71      0.54       161
   DE PIE DOBLANDO TOALLAS       0.24      0.42      0.31       161
    DE PIE MOVIENDO LIBROS       0.21      0.04      0.06       161
          DE PIE USANDO PC       0.60      0.20      0.30       161
        FASE REPOSO CON K5       0.87      0.96      0.91       161
INCREMENTAL CICLOERGOMETRO       1.00      0.94      0.97       161
           SENTADO LEYENDO       0.37      0.70      0.48       161
         SENTADO USANDO PC       0.21      0.14      0.16       161
      SENTADO VIENDO LA TV       0.77      0.15      0.25       161
   SUBIR Y BAJAR ESCALERAS       0.58      0.54      0.56       161
                    TROTAR       0.91      0.80      0.85       138

                  accuracy                           0.44      2392
                 macro avg       0.48      0.44      0.42      2392
              weighted avg       0.47      0.44      0.42      2392


Accuracy capturado en la ejecución 16: 43.94 [%]
F1-score capturado en la ejecución 16: 42.37 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-08 17:56:37.975973: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:56:37.987458: 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 00:00:1762620998.000901 1329620 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762620998.005289 1329620 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762620998.015813 1329620 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620998.015836 1329620 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620998.015838 1329620 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762620998.015840 1329620 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:56:38.019326: 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.
I0000 00:00:1762621000.274971 1329620 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621002.637675 1329747 service.cc:152] XLA service 0x7e8600013840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621002.637731 1329747 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:56:42.689133: 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 00:00:1762621003.004644 1329747 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621005.505238 1329747 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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Epoch 9/18

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Epoch 10/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 669ms/step
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:49[0m 860ms/step
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[1m 48/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m 97/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m150/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 43.14 [%]
Global F1 score (validation) = 40.32 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[0.0014807  0.0017145  0.00128037 ... 0.00186365 0.00200034 0.00091854]
 [0.0030425  0.00317218 0.00410115 ... 0.00312339 0.00182044 0.00084814]
 [0.00327187 0.0029746  0.00303607 ... 0.00302576 0.00268733 0.0008988 ]
 ...
 [0.00147338 0.0008379  0.00104394 ... 0.01981621 0.00180494 0.00690469]
 [0.00103219 0.00063495 0.00078891 ... 0.01288744 0.00099182 0.00357908]
 [0.0043056  0.00268626 0.00373175 ... 0.3715899  0.00705272 0.00217825]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 56.21 [%]
Global accuracy score (test) = 44.73 [%]
Global F1 score (train) = 53.96 [%]
Global F1 score (test) = 41.42 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.11      0.15       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.45      0.32       161
       CAMINAR USUAL SPEED       0.18      0.09      0.12       161
            CAMINAR ZIGZAG       0.21      0.11      0.14       161
          DE PIE BARRIENDO       0.43      0.70      0.53       161
   DE PIE DOBLANDO TOALLAS       0.51      0.28      0.36       161
    DE PIE MOVIENDO LIBROS       0.24      0.11      0.15       161
          DE PIE USANDO PC       0.58      0.80      0.67       161
        FASE REPOSO CON K5       0.87      0.96      0.91       161
INCREMENTAL CICLOERGOMETRO       0.96      0.96      0.96       161
           SENTADO LEYENDO       0.26      0.58      0.36       161
         SENTADO USANDO PC       0.17      0.06      0.09       161
      SENTADO VIENDO LA TV       0.41      0.11      0.18       161
   SUBIR Y BAJAR ESCALERAS       0.38      0.78      0.51       161
                    TROTAR       0.90      0.65      0.76       138

                  accuracy                           0.45      2392
                 macro avg       0.44      0.45      0.41      2392
              weighted avg       0.44      0.45      0.41      2392


Accuracy capturado en la ejecución 17: 44.73 [%]
F1-score capturado en la ejecución 17: 41.42 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-08 17:57:25.006634: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:57:25.018366: 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 00:00:1762621045.031773 1331731 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621045.035991 1331731 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621045.045853 1331731 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621045.045874 1331731 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621045.045876 1331731 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621045.045878 1331731 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:57:25.049139: 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.
I0000 00:00:1762621047.287658 1331731 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621049.627422 1331839 service.cc:152] XLA service 0x7b9594013e70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621049.627453 1331839 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:57:29.673715: 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 00:00:1762621049.988703 1331839 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621052.519459 1331839 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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[1m1007/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5100 - loss: 1.2342
[1m1036/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5101 - loss: 1.2342
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Epoch 7/18

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 22ms/step - accuracy: 0.3125 - loss: 1.6836
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[1m 113/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5169 - loss: 1.2107
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[1m 491/1093[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5315 - loss: 1.1983
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[1m 695/1093[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5336 - loss: 1.1961
[1m 724/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5338 - loss: 1.1959
[1m 746/1093[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5340 - loss: 1.1956
[1m 776/1093[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5342 - loss: 1.1953
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[1m 922/1093[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5351 - loss: 1.1944
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[1m1011/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5357 - loss: 1.1939
[1m1038/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5358 - loss: 1.1938
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Epoch 8/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
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Global accuracy score (validation) = 38.72 [%]
Global F1 score (validation) = 36.71 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.2837251e-02 7.1377363e-03 1.1049245e-02 ... 2.4450910e-03
  1.1782469e-02 1.1262901e-02]
 [4.5669517e-03 2.8235228e-03 3.4099882e-03 ... 4.5128906e-04
  2.4757059e-03 3.1810938e-03]
 [6.3938424e-03 2.8334500e-03 4.8125782e-03 ... 5.3258956e-04
  3.2294178e-03 3.3735172e-03]
 ...
 [6.6941980e-06 4.2045856e-05 1.2080335e-05 ... 1.7108521e-05
  6.9771813e-06 3.2066640e-05]
 [1.1440549e-04 7.7410811e-04 1.7667279e-04 ... 6.2525796e-04
  1.8591426e-04 2.8658888e-04]
 [1.6551951e-03 1.1588872e-03 1.1772414e-03 ... 4.5501271e-01
  6.9486368e-03 5.3380465e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 65.12 [%]
Global accuracy score (test) = 44.48 [%]
Global F1 score (train) = 64.68 [%]
Global F1 score (test) = 42.49 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.22      0.22       161
 CAMINAR CON MÓVIL O LIBRO       0.28      0.29      0.29       161
       CAMINAR USUAL SPEED       0.26      0.22      0.24       161
            CAMINAR ZIGZAG       0.20      0.32      0.25       161
          DE PIE BARRIENDO       0.60      0.17      0.27       161
   DE PIE DOBLANDO TOALLAS       0.30      0.34      0.32       161
    DE PIE MOVIENDO LIBROS       0.15      0.08      0.10       161
          DE PIE USANDO PC       0.47      0.93      0.62       161
        FASE REPOSO CON K5       0.85      0.81      0.83       161
INCREMENTAL CICLOERGOMETRO       0.92      0.93      0.92       161
           SENTADO LEYENDO       0.50      0.04      0.07       161
         SENTADO USANDO PC       0.40      0.66      0.50       161
      SENTADO VIENDO LA TV       0.48      0.50      0.49       161
   SUBIR Y BAJAR ESCALERAS       0.53      0.47      0.50       161
                    TROTAR       0.79      0.73      0.76       138

                  accuracy                           0.44      2392
                 macro avg       0.46      0.45      0.42      2392
              weighted avg       0.46      0.44      0.42      2392


Accuracy capturado en la ejecución 18: 44.48 [%]
F1-score capturado en la ejecución 18: 42.49 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-08 17:58:07.493166: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:58:07.504603: 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 00:00:1762621087.517933 1333584 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621087.522153 1333584 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621087.532066 1333584 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621087.532085 1333584 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621087.532087 1333584 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621087.532104 1333584 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:58:07.535332: 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.
I0000 00:00:1762621089.786286 1333584 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621092.151953 1333716 service.cc:152] XLA service 0x74dd28014440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621092.151987 1333716 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:58:12.197531: 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 00:00:1762621092.500238 1333716 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621094.998173 1333716 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 641ms/step
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m102/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 993us/step
[1m154/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 985us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 41.05 [%]
Global F1 score (validation) = 38.03 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[9.09796357e-03 1.14210760e-02 8.45147576e-03 ... 4.85935062e-03
  8.24737549e-03 3.79194948e-03]
 [1.44334668e-02 1.79111362e-02 1.94167793e-02 ... 1.32748419e-02
  1.78487413e-02 2.34407140e-03]
 [1.33608319e-02 1.42596299e-02 1.61973424e-02 ... 6.31552748e-03
  1.18711805e-02 2.62238318e-03]
 ...
 [4.71355015e-04 3.00904445e-04 2.99043255e-04 ... 7.38394586e-03
  2.67490279e-03 9.01860592e-04]
 [1.77903159e-03 1.27033121e-03 1.19199743e-03 ... 1.86049975e-02
  2.60746409e-03 1.59384462e-03]
 [6.99198712e-03 6.77614380e-03 6.29696855e-03 ... 3.51383865e-01
  5.83188562e-03 7.58875802e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 61.29 [%]
Global accuracy score (test) = 41.18 [%]
Global F1 score (train) = 58.93 [%]
Global F1 score (test) = 39.46 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.24      0.22       161
 CAMINAR CON MÓVIL O LIBRO       0.17      0.16      0.16       161
       CAMINAR USUAL SPEED       0.31      0.45      0.36       161
            CAMINAR ZIGZAG       0.21      0.26      0.23       161
          DE PIE BARRIENDO       0.46      0.57      0.50       161
   DE PIE DOBLANDO TOALLAS       0.34      0.53      0.42       161
    DE PIE MOVIENDO LIBROS       0.25      0.02      0.05       161
          DE PIE USANDO PC       0.65      0.66      0.65       161
        FASE REPOSO CON K5       0.49      0.89      0.63       161
INCREMENTAL CICLOERGOMETRO       1.00      0.71      0.83       161
           SENTADO LEYENDO       0.41      0.06      0.10       161
         SENTADO USANDO PC       0.26      0.14      0.19       161
      SENTADO VIENDO LA TV       0.26      0.35      0.30       161
   SUBIR Y BAJAR ESCALERAS       0.57      0.43      0.49       161
                    TROTAR       0.82      0.75      0.78       138

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


Accuracy capturado en la ejecución 19: 41.18 [%]
F1-score capturado en la ejecución 19: 39.46 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-08 17:58:49.864927: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:58:49.876362: 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 00:00:1762621129.889625 1335445 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621129.893805 1335445 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621129.903913 1335445 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621129.903934 1335445 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621129.903936 1335445 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621129.903938 1335445 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:58:49.907097: 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.
I0000 00:00:1762621132.133735 1335445 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621134.456121 1335573 service.cc:152] XLA service 0x72f614014fc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621134.456159 1335573 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:58:54.508047: 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 00:00:1762621134.820937 1335573 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621137.349413 1335573 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:22:45[0m 5s/step - accuracy: 0.0000e+00 - loss: 3.7142
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[1m  56/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1586 - loss: 2.7988
[1m  87/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1799 - loss: 2.6668
[1m 118/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1948 - loss: 2.5805
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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 23ms/step - accuracy: 0.4375 - loss: 1.4873
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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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Epoch 9/18

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Epoch 10/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 661ms/step
[1m47/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step   
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 48/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m102/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m153/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.37 [%]
Global F1 score (validation) = 37.02 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[4.4163037e-03 4.2566019e-03 1.8353839e-03 ... 1.3968132e-03
  2.5164298e-04 3.3939650e-04]
 [7.1196049e-03 7.2415010e-03 5.9357290e-03 ... 2.8902441e-03
  5.8141974e-04 5.2368687e-04]
 [7.5597414e-03 6.5290811e-03 5.9611350e-03 ... 2.6630978e-03
  4.8206080e-04 4.3029472e-04]
 ...
 [5.1355474e-03 6.0302131e-03 3.2875512e-03 ... 1.2706216e-01
  4.9931187e-02 1.3003869e-02]
 [3.1282655e-03 6.0259001e-03 1.6332823e-03 ... 7.3558152e-02
  4.1326143e-02 7.9139685e-03]
 [1.9342611e-03 5.0293552e-03 1.9195966e-03 ... 2.6436073e-01
  1.9754775e-02 3.4898359e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 60.31 [%]
Global accuracy score (test) = 42.27 [%]
Global F1 score (train) = 58.27 [%]
Global F1 score (test) = 41.14 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.32      0.26       161
 CAMINAR CON MÓVIL O LIBRO       0.32      0.23      0.27       161
       CAMINAR USUAL SPEED       0.19      0.07      0.10       161
            CAMINAR ZIGZAG       0.18      0.37      0.25       161
          DE PIE BARRIENDO       0.50      0.48      0.49       161
   DE PIE DOBLANDO TOALLAS       0.33      0.64      0.44       161
    DE PIE MOVIENDO LIBROS       0.31      0.12      0.17       161
          DE PIE USANDO PC       0.70      0.40      0.51       161
        FASE REPOSO CON K5       0.81      0.88      0.85       161
INCREMENTAL CICLOERGOMETRO       1.00      0.71      0.83       161
           SENTADO LEYENDO       0.57      0.26      0.36       161
         SENTADO USANDO PC       0.30      0.74      0.42       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.43      0.42      0.42       161
                    TROTAR       0.88      0.75      0.81       138

                  accuracy                           0.42      2392
                 macro avg       0.45      0.43      0.41      2392
              weighted avg       0.44      0.42      0.41      2392


Accuracy capturado en la ejecución 20: 42.27 [%]
F1-score capturado en la ejecución 20: 41.14 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-08 17:59:36.633706: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 17:59:36.645469: 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 00:00:1762621176.659154 1337568 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621176.663435 1337568 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621176.673430 1337568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621176.673451 1337568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621176.673453 1337568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621176.673455 1337568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:59:36.676733: 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.
I0000 00:00:1762621178.932794 1337568 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621181.271221 1337674 service.cc:152] XLA service 0x79c27c014d00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621181.271295 1337674 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:59:41.319252: 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 00:00:1762621181.620176 1337674 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621184.133245 1337674 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:22:18[0m 5s/step - accuracy: 0.0625 - loss: 3.2412
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[1m  58/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1613 - loss: 2.7306
[1m  85/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1827 - loss: 2.6165
[1m 112/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1971 - loss: 2.5342
[1m 141/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2090 - loss: 2.4680
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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 54/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 949us/step
[1m109/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 932us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 39.58 [%]
Global F1 score (validation) = 37.58 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[1.5431081e-03 4.4781999e-03 8.5559773e-04 ... 1.8616482e-03
  1.1182573e-03 4.5877675e-04]
 [3.4496046e-03 7.7855606e-03 2.7130900e-03 ... 4.6033235e-03
  3.6429353e-03 8.2350249e-04]
 [2.1414480e-03 4.8188665e-03 1.3448318e-03 ... 1.8334015e-03
  1.5646815e-03 6.6252123e-04]
 ...
 [1.3540950e-04 3.5281359e-05 5.6298053e-05 ... 5.2278879e-04
  1.2140762e-04 2.0034284e-04]
 [9.1814225e-05 5.2170577e-05 4.5399651e-05 ... 7.6878036e-04
  2.0588654e-04 3.2708116e-04]
 [2.0008781e-03 1.3598485e-03 2.2067353e-03 ... 2.7066615e-01
  3.6247015e-02 2.0801800e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 64.26 [%]
Global accuracy score (test) = 41.14 [%]
Global F1 score (train) = 62.87 [%]
Global F1 score (test) = 39.08 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.16      0.19      0.17       161
 CAMINAR CON MÓVIL O LIBRO       0.26      0.52      0.34       161
       CAMINAR USUAL SPEED       0.35      0.18      0.24       161
            CAMINAR ZIGZAG       0.22      0.19      0.21       161
          DE PIE BARRIENDO       0.45      0.62      0.52       161
   DE PIE DOBLANDO TOALLAS       0.26      0.48      0.33       161
    DE PIE MOVIENDO LIBROS       0.13      0.07      0.09       161
          DE PIE USANDO PC       1.00      0.03      0.06       161
        FASE REPOSO CON K5       0.86      0.85      0.85       161
INCREMENTAL CICLOERGOMETRO       1.00      0.95      0.97       161
           SENTADO LEYENDO       0.34      0.74      0.46       161
         SENTADO USANDO PC       0.36      0.15      0.21       161
      SENTADO VIENDO LA TV       0.41      0.10      0.16       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.43      0.47       161
                    TROTAR       0.83      0.70      0.76       138

                  accuracy                           0.41      2392
                 macro avg       0.48      0.41      0.39      2392
              weighted avg       0.47      0.41      0.39      2392


Accuracy capturado en la ejecución 21: 41.14 [%]
F1-score capturado en la ejecución 21: 39.08 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-08 18:00:16.618922: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 18:00:16.630132: 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 00:00:1762621216.643149 1339388 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621216.647307 1339388 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621216.656983 1339388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621216.657001 1339388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621216.657003 1339388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621216.657005 1339388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 18:00:16.660148: 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.
I0000 00:00:1762621218.912462 1339388 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621221.237773 1339496 service.cc:152] XLA service 0x7e3710002970 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621221.237805 1339496 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 18:00:21.283229: 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 00:00:1762621221.582251 1339496 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621224.092367 1339496 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 645ms/step
[1m51/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:39[0m 842ms/step
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[1m158/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 962us/step
[1m206/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 983us/step
[1m257/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 984us/step
[1m308/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 985us/step
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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m103/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 988us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.65 [%]
Global F1 score (validation) = 38.68 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[6.24532904e-03 7.11543206e-03 2.96712364e-03 ... 8.51661712e-03
  1.58906933e-02 1.95989776e-02]
 [3.48654110e-03 8.13525170e-03 4.36303997e-03 ... 2.26141494e-02
  2.02571917e-02 9.70889814e-03]
 [4.58123907e-03 6.44972688e-03 3.86309018e-03 ... 1.02447495e-02
  1.35215297e-02 7.88424443e-03]
 ...
 [1.27323176e-04 3.41525854e-04 2.18044719e-04 ... 5.56405447e-03
  1.40180462e-03 7.73413980e-04]
 [9.23859043e-05 1.50133274e-04 1.03598330e-04 ... 3.75822099e-04
  1.75672467e-04 3.06378497e-04]
 [3.14074190e-04 1.29163137e-03 7.77137757e-04 ... 2.13129565e-01
  2.78808698e-02 1.60463899e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 59.28 [%]
Global accuracy score (test) = 46.45 [%]
Global F1 score (train) = 57.36 [%]
Global F1 score (test) = 43.97 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.44      0.31       161
 CAMINAR CON MÓVIL O LIBRO       0.28      0.38      0.32       161
       CAMINAR USUAL SPEED       0.27      0.12      0.17       161
            CAMINAR ZIGZAG       0.14      0.11      0.12       161
          DE PIE BARRIENDO       0.45      0.76      0.56       161
   DE PIE DOBLANDO TOALLAS       0.41      0.27      0.33       161
    DE PIE MOVIENDO LIBROS       0.25      0.09      0.13       161
          DE PIE USANDO PC       0.62      0.75      0.68       161
        FASE REPOSO CON K5       0.86      0.86      0.86       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.38      0.12      0.18       161
         SENTADO USANDO PC       0.31      0.80      0.44       161
      SENTADO VIENDO LA TV       1.00      0.06      0.11       161
   SUBIR Y BAJAR ESCALERAS       0.60      0.51      0.55       161
                    TROTAR       0.93      0.80      0.86       138

                  accuracy                           0.46      2392
                 macro avg       0.51      0.47      0.44      2392
              weighted avg       0.51      0.46      0.44      2392


Accuracy capturado en la ejecución 22: 46.45 [%]
F1-score capturado en la ejecución 22: 43.97 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-08 18:00:58.897234: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 18:00:58.908498: 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 00:00:1762621258.921761 1341248 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621258.925720 1341248 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621258.935682 1341248 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621258.935702 1341248 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621258.935711 1341248 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621258.935712 1341248 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 18:00:58.938694: 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.
I0000 00:00:1762621261.197667 1341248 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621263.522177 1341374 service.cc:152] XLA service 0x75e808013f60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621263.522217 1341374 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 18:01:03.571765: 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 00:00:1762621263.884705 1341374 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621266.407511 1341374 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:22:31[0m 5s/step - accuracy: 0.0625 - loss: 3.9544
[1m  28/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 2ms/step - accuracy: 0.1328 - loss: 2.9985    
[1m  57/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1654 - loss: 2.7650
[1m  90/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1921 - loss: 2.6245
[1m 123/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2071 - loss: 2.5361
[1m 152/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2157 - loss: 2.4792
[1m 180/1093[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2225 - loss: 2.4346
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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 645ms/step
[1m46/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step   
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:48[0m 858ms/step
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[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 52/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 995us/step
[1m101/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step  
[1m150/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 42.77 [%]
Global F1 score (validation) = 40.42 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[2.45663570e-03 9.14846268e-03 2.54686619e-03 ... 3.18538956e-03
  5.28372778e-03 2.94529041e-03]
 [4.37739259e-03 1.55506069e-02 9.93450265e-03 ... 9.19880252e-03
  7.82910734e-03 1.08260126e-03]
 [2.31056334e-03 7.77997449e-03 2.12741457e-03 ... 2.05972046e-03
  2.87257251e-03 1.61664293e-03]
 ...
 [9.09091614e-06 1.36500375e-05 1.49034786e-05 ... 1.91776271e-05
  3.25122637e-05 1.67878068e-04]
 [2.94987834e-03 1.85362273e-03 2.43892171e-03 ... 5.63888587e-02
  8.20657238e-03 1.90115487e-03]
 [3.14511941e-03 1.64141785e-03 2.51805084e-03 ... 4.09016550e-01
  6.98099798e-03 9.61779908e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 62.11 [%]
Global accuracy score (test) = 44.02 [%]
Global F1 score (train) = 60.11 [%]
Global F1 score (test) = 43.15 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.21      0.21       161
 CAMINAR CON MÓVIL O LIBRO       0.24      0.42      0.31       161
       CAMINAR USUAL SPEED       0.36      0.19      0.25       161
            CAMINAR ZIGZAG       0.22      0.17      0.19       161
          DE PIE BARRIENDO       0.49      0.33      0.39       161
   DE PIE DOBLANDO TOALLAS       0.31      0.68      0.43       161
    DE PIE MOVIENDO LIBROS       0.29      0.08      0.13       161
          DE PIE USANDO PC       0.59      0.52      0.55       161
        FASE REPOSO CON K5       0.86      0.85      0.85       161
INCREMENTAL CICLOERGOMETRO       1.00      0.96      0.98       161
           SENTADO LEYENDO       0.74      0.14      0.24       161
         SENTADO USANDO PC       0.24      0.26      0.25       161
      SENTADO VIENDO LA TV       0.35      0.54      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.44      0.54      0.49       161
                    TROTAR       0.82      0.75      0.78       138

                  accuracy                           0.44      2392
                 macro avg       0.48      0.44      0.43      2392
              weighted avg       0.47      0.44      0.43      2392


Accuracy capturado en la ejecución 23: 44.02 [%]
F1-score capturado en la ejecución 23: 43.15 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-08 18:01:40.809349: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 18:01:40.820698: 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 00:00:1762621300.833974 1343102 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621300.838030 1343102 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621300.848298 1343102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621300.848319 1343102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621300.848322 1343102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621300.848323 1343102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 18:01:40.851505: 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.
I0000 00:00:1762621303.117043 1343102 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621305.447600 1343232 service.cc:152] XLA service 0x7e8ea4015bb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621305.447634 1343232 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 18:01:45.493041: 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 00:00:1762621305.794992 1343232 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621308.303055 1343232 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m104/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 979us/step
[1m156/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 975us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 40.95 [%]
Global F1 score (validation) = 38.71 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.19976346e-03 7.23964395e-03 2.01683934e-03 ... 5.21207112e-04
  2.69742683e-04 2.93627818e-04]
 [1.96093339e-02 9.33004450e-03 2.12976355e-02 ... 2.26441561e-03
  2.36246176e-03 1.99048826e-03]
 [1.10183405e-02 6.45634253e-03 9.02459677e-03 ... 1.07441028e-03
  6.74412062e-04 7.63502379e-04]
 ...
 [6.15493627e-05 5.05896460e-05 5.58789870e-05 ... 1.18359574e-04
  2.60712230e-04 1.69824809e-04]
 [9.75748771e-05 7.34318164e-05 8.73378594e-05 ... 2.04752971e-04
  4.83835407e-04 2.34125531e-04]
 [6.11580443e-03 3.77329253e-03 5.89345954e-03 ... 1.93146840e-01
  3.73980887e-02 4.84261569e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 60.51 [%]
Global accuracy score (test) = 46.95 [%]
Global F1 score (train) = 58.16 [%]
Global F1 score (test) = 43.68 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.20      0.23       161
 CAMINAR CON MÓVIL O LIBRO       0.24      0.66      0.35       161
       CAMINAR USUAL SPEED       0.00      0.00      0.00       161
            CAMINAR ZIGZAG       0.12      0.06      0.08       161
          DE PIE BARRIENDO       0.65      0.47      0.54       161
   DE PIE DOBLANDO TOALLAS       0.37      0.71      0.48       161
    DE PIE MOVIENDO LIBROS       0.04      0.01      0.01       161
          DE PIE USANDO PC       0.65      0.74      0.69       161
        FASE REPOSO CON K5       0.87      0.96      0.91       161
INCREMENTAL CICLOERGOMETRO       0.87      0.91      0.89       161
           SENTADO LEYENDO       0.32      0.42      0.36       161
         SENTADO USANDO PC       0.32      0.16      0.21       161
      SENTADO VIENDO LA TV       0.49      0.39      0.44       161
   SUBIR Y BAJAR ESCALERAS       0.46      0.67      0.54       161
                    TROTAR       0.92      0.72      0.81       138

                  accuracy                           0.47      2392
                 macro avg       0.44      0.47      0.44      2392
              weighted avg       0.43      0.47      0.43      2392


Accuracy capturado en la ejecución 24: 46.95 [%]
F1-score capturado en la ejecución 24: 43.68 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-08 18:02:20.530412: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 18:02:20.541684: 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 00:00:1762621340.554853 1344853 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621340.558949 1344853 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621340.568690 1344853 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621340.568708 1344853 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621340.568711 1344853 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621340.568712 1344853 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 18:02:20.571831: 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.
I0000 00:00:1762621342.806467 1344853 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621345.101494 1344988 service.cc:152] XLA service 0x7bcee0003000 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621345.101538 1344988 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 18:02:25.148526: 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 00:00:1762621345.461930 1344988 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621347.987580 1344988 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Epoch 8/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 643ms/step
[1m49/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:50[0m 862ms/step
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[1m 51/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m102/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m157/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 972us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.24 [%]
Global F1 score (validation) = 35.87 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[2.1346393e-03 4.1340217e-03 1.7010409e-03 ... 1.6671878e-03
  1.7197927e-03 2.1585478e-03]
 [5.7766461e-03 8.5909152e-03 4.9164467e-03 ... 4.7788271e-03
  4.8805736e-03 7.5150393e-03]
 [3.4047905e-03 4.4722673e-03 3.1768307e-03 ... 2.6784874e-03
  2.8199609e-03 3.3352124e-03]
 ...
 [1.2671892e-03 1.1762244e-03 9.6148101e-04 ... 2.5137629e-02
  7.9477234e-03 4.8708068e-03]
 [2.4855277e-03 1.4322627e-03 2.3707130e-03 ... 1.4522267e-02
  2.3035347e-03 9.9087146e-04]
 [2.7248007e-03 8.9485862e-04 8.5839065e-04 ... 3.4955171e-01
  1.6518794e-03 1.9590183e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 59.51 [%]
Global accuracy score (test) = 41.1 [%]
Global F1 score (train) = 58.46 [%]
Global F1 score (test) = 38.76 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.51      0.32       161
 CAMINAR CON MÓVIL O LIBRO       0.27      0.24      0.25       161
       CAMINAR USUAL SPEED       0.35      0.22      0.27       161
            CAMINAR ZIGZAG       0.24      0.16      0.19       161
          DE PIE BARRIENDO       0.55      0.62      0.58       161
   DE PIE DOBLANDO TOALLAS       0.31      0.52      0.39       161
    DE PIE MOVIENDO LIBROS       0.32      0.34      0.33       161
          DE PIE USANDO PC       0.90      0.06      0.11       161
        FASE REPOSO CON K5       0.80      0.57      0.67       161
INCREMENTAL CICLOERGOMETRO       1.00      0.93      0.96       161
           SENTADO LEYENDO       0.00      0.00      0.00       161
         SENTADO USANDO PC       0.29      0.75      0.42       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.57      0.48      0.52       161
                    TROTAR       0.80      0.83      0.81       138

                  accuracy                           0.41      2392
                 macro avg       0.44      0.41      0.39      2392
              weighted avg       0.44      0.41      0.38      2392


Accuracy capturado en la ejecución 25: 41.1 [%]
F1-score capturado en la ejecución 25: 38.76 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-08 18:03:02.740771: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 18:03:02.752287: 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 00:00:1762621382.766183 1346711 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621382.770205 1346711 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621382.780257 1346711 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621382.780278 1346711 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621382.780280 1346711 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621382.780282 1346711 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 18:03:02.783440: 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.
I0000 00:00:1762621385.030352 1346711 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621387.342180 1346844 service.cc:152] XLA service 0x737588003d20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621387.342219 1346844 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 18:03:07.395039: 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 00:00:1762621387.702010 1346844 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621390.236528 1346844 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:22:22[0m 5s/step - accuracy: 0.1250 - loss: 3.3834
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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m152/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
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Global accuracy score (validation) = 40.51 [%]
Global F1 score (validation) = 38.12 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[7.95187149e-03 6.97599212e-03 9.28357337e-03 ... 5.16581768e-03
  4.84253094e-03 4.24769707e-03]
 [6.43661246e-03 9.58806928e-03 1.22809559e-02 ... 9.32588056e-03
  7.19645200e-03 1.81378284e-03]
 [4.45896853e-03 5.21453703e-03 7.49248127e-03 ... 6.30016206e-03
  5.72053390e-03 1.80233049e-03]
 ...
 [1.85959361e-05 1.09465918e-05 9.60876969e-06 ... 1.01301139e-05
  6.30330442e-06 8.63610767e-05]
 [1.09764778e-05 8.12340386e-06 6.06752974e-06 ... 1.11008985e-05
  1.21738958e-05 1.00339581e-04]
 [4.42058733e-03 4.23752749e-03 1.70221541e-03 ... 2.79999256e-01
  2.53328662e-02 3.70528735e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 64.9 [%]
Global accuracy score (test) = 47.99 [%]
Global F1 score (train) = 63.5 [%]
Global F1 score (test) = 46.22 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.42      0.29       161
 CAMINAR CON MÓVIL O LIBRO       0.27      0.27      0.27       161
       CAMINAR USUAL SPEED       0.37      0.24      0.29       161
            CAMINAR ZIGZAG       0.14      0.12      0.13       161
          DE PIE BARRIENDO       0.52      0.43      0.47       161
   DE PIE DOBLANDO TOALLAS       0.42      0.49      0.45       161
    DE PIE MOVIENDO LIBROS       0.17      0.01      0.01       161
          DE PIE USANDO PC       0.49      0.89      0.63       161
        FASE REPOSO CON K5       0.86      0.84      0.85       161
INCREMENTAL CICLOERGOMETRO       0.92      0.95      0.93       161
           SENTADO LEYENDO       0.65      0.29      0.40       161
         SENTADO USANDO PC       0.41      0.80      0.54       161
      SENTADO VIENDO LA TV       0.58      0.25      0.35       161
   SUBIR Y BAJAR ESCALERAS       0.52      0.52      0.52       161
                    TROTAR       0.91      0.71      0.80       138

                  accuracy                           0.48      2392
                 macro avg       0.49      0.48      0.46      2392
              weighted avg       0.49      0.48      0.46      2392


Accuracy capturado en la ejecución 26: 47.99 [%]
F1-score capturado en la ejecución 26: 46.22 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-08 18:03:42.395626: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 18:03:42.407272: 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 00:00:1762621422.421178 1348463 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621422.425530 1348463 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621422.435495 1348463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621422.435517 1348463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621422.435519 1348463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621422.435520 1348463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 18:03:42.438687: 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.
I0000 00:00:1762621424.675153 1348463 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621426.985037 1348594 service.cc:152] XLA service 0x730990015690 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621426.985067 1348594 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 18:03:47.038691: 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 00:00:1762621427.336599 1348594 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621429.827967 1348594 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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[1m1011/1093[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5576 - loss: 1.1417
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[1m1093/1093[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.5579 - loss: 1.1422 - val_accuracy: 0.4235 - val_loss: 1.7952
Epoch 8/18

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[1m  59/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5862 - loss: 1.0964
[1m  87/1093[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5888 - loss: 1.0850
[1m 117/1093[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5863 - loss: 1.0844
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[1m 863/1093[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5813 - loss: 1.0931
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Epoch 9/18

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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

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[1m 98/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m152/159[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
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Global accuracy score (validation) = 43.06 [%]
Global F1 score (validation) = 41.4 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[2.8588856e-03 5.7795565e-03 2.2011704e-03 ... 1.5998340e-03
  3.4210326e-03 1.5221179e-03]
 [5.1293322e-03 3.5090316e-03 3.0096443e-03 ... 3.9415834e-03
  4.7105527e-03 1.1315605e-03]
 [3.4339784e-03 4.0184776e-03 1.9340202e-03 ... 1.7877425e-03
  3.4345647e-03 9.2463859e-04]
 ...
 [6.6598266e-04 5.9830589e-04 7.5371435e-04 ... 1.5793784e-04
  9.3035756e-05 1.4321608e-04]
 [4.9298978e-04 3.5976007e-04 6.0898182e-04 ... 1.1355202e-04
  3.7795286e-05 8.1897029e-05]
 [4.7089695e-03 1.6420170e-03 1.9705230e-03 ... 3.8304630e-01
  7.3744059e-03 3.5917338e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 60.18 [%]
Global accuracy score (test) = 45.78 [%]
Global F1 score (train) = 57.94 [%]
Global F1 score (test) = 44.15 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.15      0.19      0.16       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.46      0.32       161
       CAMINAR USUAL SPEED       0.22      0.29      0.25       161
            CAMINAR ZIGZAG       0.05      0.01      0.01       161
          DE PIE BARRIENDO       0.51      0.49      0.50       161
   DE PIE DOBLANDO TOALLAS       0.30      0.43      0.35       161
    DE PIE MOVIENDO LIBROS       0.29      0.16      0.20       161
          DE PIE USANDO PC       0.55      0.61      0.58       161
        FASE REPOSO CON K5       0.86      0.88      0.87       161
INCREMENTAL CICLOERGOMETRO       1.00      0.96      0.98       161
           SENTADO LEYENDO       0.58      0.04      0.08       161
         SENTADO USANDO PC       0.67      0.30      0.42       161
      SENTADO VIENDO LA TV       0.39      0.83      0.53       161
   SUBIR Y BAJAR ESCALERAS       0.56      0.57      0.56       161
                    TROTAR       0.94      0.70      0.80       138

                  accuracy                           0.46      2392
                 macro avg       0.49      0.46      0.44      2392
              weighted avg       0.48      0.46      0.44      2392


Accuracy capturado en la ejecución 27: 45.78 [%]
F1-score capturado en la ejecución 27: 44.15 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-08 18:04:26.735168: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 18:04:26.746426: 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 00:00:1762621466.759519 1350458 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621466.763673 1350458 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621466.773373 1350458 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621466.773391 1350458 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621466.773393 1350458 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621466.773395 1350458 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 18:04:26.776534: 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.
I0000 00:00:1762621469.002203 1350458 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621471.318781 1350589 service.cc:152] XLA service 0x744f34008470 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621471.318817 1350589 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 18:04:31.363628: 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 00:00:1762621471.665802 1350589 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621474.194090 1350589 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

[1m   1/1093[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1685
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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 643ms/step
[1m50/75[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step   
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Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:39[0m 842ms/step
[1m 45/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step    
[1m102/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 998us/step
[1m154/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 990us/step
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[1m259/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 977us/step
[1m309/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 983us/step
[1m361/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 982us/step
[1m411/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 985us/step
[1m463/547[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 986us/step
[1m521/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 972us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m42/75[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m100/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m149/159[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.34 [%]
Global F1 score (validation) = 38.14 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[2.47491570e-03 3.75989289e-03 1.99996005e-03 ... 1.49342848e-03
  4.43956902e-04 3.01993598e-04]
 [2.94230320e-03 4.17064829e-03 2.00201990e-03 ... 2.70718592e-03
  8.37665459e-04 3.60365113e-04]
 [2.53165863e-03 2.69686850e-03 1.74797012e-03 ... 1.48584100e-03
  5.99802413e-04 3.68457084e-04]
 ...
 [1.01663427e-05 1.24887799e-06 1.05932293e-06 ... 3.34495599e-05
  9.81541325e-06 3.60374725e-06]
 [3.35735640e-05 4.97605561e-06 4.74245599e-06 ... 1.17202595e-04
  2.97743027e-05 1.80061616e-05]
 [9.76100215e-04 7.90797290e-04 1.24966737e-03 ... 3.09095681e-01
  1.75381708e-03 6.76902302e-04]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 61.72 [%]
Global accuracy score (test) = 42.27 [%]
Global F1 score (train) = 60.38 [%]
Global F1 score (test) = 41.18 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.16      0.10      0.12       161
 CAMINAR CON MÓVIL O LIBRO       0.23      0.63      0.34       161
       CAMINAR USUAL SPEED       0.42      0.24      0.30       161
            CAMINAR ZIGZAG       0.19      0.11      0.14       161
          DE PIE BARRIENDO       0.42      0.59      0.49       161
   DE PIE DOBLANDO TOALLAS       0.26      0.45      0.33       161
    DE PIE MOVIENDO LIBROS       0.11      0.01      0.02       161
          DE PIE USANDO PC       0.65      0.38      0.48       161
        FASE REPOSO CON K5       0.86      0.89      0.87       161
INCREMENTAL CICLOERGOMETRO       1.00      0.88      0.93       161
           SENTADO LEYENDO       0.29      0.58      0.39       161
         SENTADO USANDO PC       0.26      0.14      0.19       161
      SENTADO VIENDO LA TV       0.40      0.19      0.26       161
   SUBIR Y BAJAR ESCALERAS       0.55      0.51      0.53       161
                    TROTAR       0.94      0.68      0.79       138

                  accuracy                           0.42      2392
                 macro avg       0.45      0.43      0.41      2392
              weighted avg       0.44      0.42      0.41      2392


Accuracy capturado en la ejecución 28: 42.27 [%]
F1-score capturado en la ejecución 28: 41.18 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-08 18:05:04.160176: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-08 18:05:04.171456: 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 00:00:1762621504.184610 1352088 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762621504.188717 1352088 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762621504.198583 1352088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621504.198601 1352088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621504.198603 1352088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762621504.198605 1352088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 18:05:04.201867: 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.
I0000 00:00:1762621506.413850 1352088 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Epoch 1/18
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762621508.730633 1352218 service.cc:152] XLA service 0x78ade0015610 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762621508.730683 1352218 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 18:05:08.780210: 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 00:00:1762621509.105000 1352218 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762621511.631043 1352218 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/18

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Epoch 3/18

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Epoch 4/18

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Epoch 5/18

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Epoch 6/18

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Epoch 7/18

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[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.
['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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:46[0m 855ms/step
[1m 39/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step    
[1m 92/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step
[1m144/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step
[1m194/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step
[1m245/547[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step
[1m300/547[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step
[1m356/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 993us/step
[1m413/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 977us/step
[1m470/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 966us/step
[1m525/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 960us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m47/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 49/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m105/159[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 971us/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 42.23 [%]
Global F1 score (validation) = 39.31 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.0276886e-03 4.1294708e-03 2.8797782e-03 ... 1.5081246e-03
  1.2135045e-02 6.0400427e-03]
 [6.3909404e-03 5.5252542e-03 6.5812678e-03 ... 3.8676697e-03
  1.4152524e-02 1.0899455e-02]
 [6.2873466e-03 4.4247746e-03 6.1376137e-03 ... 3.1475537e-03
  1.4626152e-02 1.1464423e-02]
 ...
 [2.1446856e-05 9.8289147e-06 2.3610111e-05 ... 9.3910290e-05
  8.4382700e-05 1.6914429e-04]
 [5.1996896e-05 2.8005637e-05 5.3680309e-05 ... 4.6979613e-04
  2.2607908e-04 3.3984642e-04]
 [2.5196136e-03 2.7884261e-03 2.5225780e-03 ... 1.0985139e-01
  3.2377254e-02 5.0137630e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 58.84 [%]
Global accuracy score (test) = 47.07 [%]
Global F1 score (train) = 56.9 [%]
Global F1 score (test) = 45.26 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.19      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.24      0.58      0.34       161
       CAMINAR USUAL SPEED       0.32      0.35      0.34       161
            CAMINAR ZIGZAG       0.10      0.07      0.08       161
          DE PIE BARRIENDO       0.44      0.53      0.48       161
   DE PIE DOBLANDO TOALLAS       0.32      0.30      0.31       161
    DE PIE MOVIENDO LIBROS       0.36      0.15      0.21       161
          DE PIE USANDO PC       0.59      0.85      0.69       161
        FASE REPOSO CON K5       0.86      0.87      0.86       161
INCREMENTAL CICLOERGOMETRO       0.95      0.96      0.95       161
           SENTADO LEYENDO       0.63      0.45      0.52       161
         SENTADO USANDO PC       0.39      0.76      0.52       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.44      0.52       161
                    TROTAR       0.94      0.58      0.72       138

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


Accuracy capturado en la ejecución 29: 47.07 [%]
F1-score capturado en la ejecución 29: 45.26 [%]

=== 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}
Loaded model from disk.
(2392, 3, 250)
(17480, 3, 250)

[1m  1/547[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:34[0m 833ms/step
[1m 48/547[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step    
[1m102/547[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step
[1m159/547[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 960us/step
[1m216/547[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 942us/step
[1m265/547[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 957us/step
[1m314/547[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 967us/step
[1m366/547[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 967us/step
[1m422/547[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 958us/step
[1m476/547[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 955us/step
[1m531/547[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 951us/step
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m547/547[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/75[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m48/75[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
[1m75/75[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/159[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 50/159[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m 99/159[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m143/159[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[1m159/159[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 42.92 [%]
Global F1 score (validation) = 40.14 [%]
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[[5.41033084e-03 3.66827263e-03 2.09736498e-03 ... 1.35043019e-03
  1.36806595e-03 2.26744072e-04]
 [8.09904374e-03 6.19068788e-03 2.82428460e-03 ... 2.89405114e-03
  3.08625028e-03 3.65259766e-04]
 [8.80901609e-03 4.30905726e-03 2.67942785e-03 ... 2.41875066e-03
  4.08424670e-03 5.01654460e-04]
 ...
 [2.85568076e-05 3.63085201e-05 2.85491005e-05 ... 2.47699325e-04
  4.18565432e-05 5.46400879e-05]
 [5.97861836e-05 1.10385481e-04 1.07935492e-04 ... 1.21083474e-04
  2.02517404e-05 4.63543547e-05]
 [1.26703840e-03 2.02762010e-03 2.67220940e-03 ... 1.81720138e-01
  3.74420322e-02 2.62438459e-03]]
(2392, 15)
-------------------------------------------------

Global accuracy score (train) = 64.24 [%]
Global accuracy score (test) = 44.94 [%]
Global F1 score (train) = 61.43 [%]
Global F1 score (test) = 42.0 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.42      0.27       161
 CAMINAR CON MÓVIL O LIBRO       0.25      0.20      0.22       161
       CAMINAR USUAL SPEED       0.37      0.16      0.22       161
            CAMINAR ZIGZAG       0.15      0.13      0.14       161
          DE PIE BARRIENDO       0.49      0.49      0.49       161
   DE PIE DOBLANDO TOALLAS       0.37      0.62      0.46       161
    DE PIE MOVIENDO LIBROS       0.28      0.06      0.10       161
          DE PIE USANDO PC       0.66      0.67      0.66       161
        FASE REPOSO CON K5       0.86      0.85      0.85       161
INCREMENTAL CICLOERGOMETRO       1.00      0.94      0.97       161
           SENTADO LEYENDO       0.34      0.86      0.48       161
         SENTADO USANDO PC       0.20      0.06      0.09       161
      SENTADO VIENDO LA TV       0.00      0.00      0.00       161
   SUBIR Y BAJAR ESCALERAS       0.51      0.57      0.54       161
                    TROTAR       0.84      0.75      0.79       138

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


Accuracy capturado en la ejecución 30: 44.94 [%]
F1-score capturado en la ejecución 30: 42.0 [%]

=== RESUMEN FINAL ===
Accuracies: [44.61, 42.47, 41.39, 44.11, 45.94, 45.48, 42.02, 42.93, 45.4, 44.31, 45.48, 43.02, 44.23, 41.18, 40.47, 43.94, 44.73, 44.48, 41.18, 42.27, 41.14, 46.45, 44.02, 46.95, 41.1, 47.99, 45.78, 42.27, 47.07, 44.94]
F1-scores: [42.56, 41.27, 40.88, 41.54, 44.4, 43.38, 39.2, 39.28, 43.9, 44.44, 43.48, 40.36, 42.09, 39.01, 38.59, 42.37, 41.42, 42.49, 39.46, 41.14, 39.08, 43.97, 43.15, 43.68, 38.76, 46.22, 44.15, 41.18, 45.26, 42.0]
Accuracy mean: 43.9117 | std: 2.0116
F1 mean: 41.9570 | std: 2.0726

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_CAPTURE24_acc_17_classes/metrics_test.npz
